The form and reason for exchange in archaic societies.x

Quickly copy and paste Unicode whitespace characters — and learn how and when to use them.

This is a note to call ideal fence

This is in bold
This is in italic

Everyone keeps telling you that you need to practice your fundamentals. What the hell does that mean, and how do you do it? This subreddit's all about concrete exercises that you can do to improve your fundamentals. We'll give you homework and we'll tell you where you're going right and wrong.

In this video we show you how to replace an existing heating element with a new one using Shark Bite fittings. This is a fast easy way to swap a new heating element into your baseboard if the one that you currently have is getting old and has seen better days.
#homerepair #baseboardheat #sharkbite

In this video I show you how to clean hot water baseboard heaters from the inside out in preparation for using an ozone generator to tackle odors. These simple cleaning steps can also be an essential annual maintenance of your baseboard heaters and will not only reduce musty household odors, but will also improve your baseboard heater efficiency. I will show you how to remove your radiator covers to clean all the dust and oils that can build up around your baseboard heater fins.

https://fortune.com/2025/11/11/why-ai-adoption-is-failing-seven-mistakes/
November 11, 2025

Mistake #1: The business goal isn’t crystal clear
The fix: Be precise. Be clear. Take the time up front to crystallize the problem and expected ROI with all stakeholders right off the bat.

Mistake #2: The project is poorly managed

Mistake #3: You’re overpromising. Believing AI will solve everything is a recipe for disappointment

Mistake #4: Vastly underestimating the resources required

Mistake #5: Ignoring reality

Mistake #6: No offense, but your data quality is bad

Mistake #7: Think the project’s done? Not quite
While AI projects may have a clear start and finish, the work doesn’t end when the model is operationalized. AI systems are dynamic and models can drift, data can evolve and outputs can degrade over time. Treating AI like a “set it and forget it” initiative is a costly mistake. Without continuous monitoring, evaluation, and updates, your AI solution may lose accuracy, relevance, and trustworthiness.

“Yet each man kills the thing he loves,
By each let this be heard,
Some do it with a bitter look,
Some with a flattering word,
The coward does it with a kiss,
The brave man with a sword!

Some kill their love when they are young,
And some when they are old;
Some strangle with the hands of Gold:
The kindest use a knife, because
The dead so soon grow cold.

Some love too little, some too long,
Some sell and others buy;
Some do the deed with many tears,
And some without a sigh:
For each man kills the thing he loves,
Yet each man does not die.”

― Oscar Wilde, Selected Poems of Oscar Wilde including the Ballad of Reading Gaol

AI is bad at making things that are great. It's also bad at making things that are bad. The second half is the part worth talking about.

The trap isn’t AI. The trap is that 7 is easier to reach than it’s ever been, and 7 feels like enough.

The middle has never been more crowded. The interesting work is on the edges — the 1s and the 9s — and only one of those edges is still accessible to a tool. The other one is accessible to you, if you remember how to get there.

The skill now might just be knowing what a 1 used to feel like. And being a little suspicious of anything that lands at a 5.

Compare Google Fonts side-by-side with anatomy overlays, OpenType metrics, pairing scores, and ready-to-use CSS. Free tool for designers and developers.

Stateless (JWT)

How it works:

User logs in, server creates a JWT containing user data
Server signs the JWT with a secret key
Server sends JWT to client
Client sends JWT with every request
Server verifies the signature and trusts the data inside
Why it scales:

Any server can validate a JWT using the shared secret key
No database lookup needed per request
If you add 100 more servers, they all work immediately

Today, I will focus mostly on Time Complexity from the Perl point of view.

1. O(1) - Constant Time
2. O(n) - Linear Time
3. O(log n) - Logarithmic Time
4. O(n ^ 2) - Quadratic Time
5. O(n log n) - Linearithmic Time
6. O(sqrt n) - Square Root Time
7. O(n ^ 3) - Cube Time
8. O(2 ^ n) - Exponential Time
9. O(n!) - Factorial Time

Googles system prompt of the CLI is heavily flawed IMO. That's where most problems arise. I am using a custom one. At least it is highly configurable. The vanilla Gemini Cli is not usable for me. For instance, they have a section they introduce with Proactiveness where they give advice about what is Persistence (in the agent loop). This wrong wording alone causes the agent to be "proactive" in many ways that is not wanted. After I asked the model itself once, why it started to scan my entire codebase when I just asked to create a new branch, it directly said to me there is "Proactiveness" in its system prompt. I was baffled, why would anybody want that? So i digged deeper and did also a thorough analysis with Opus 4.7 of just this system prompt file - that cost me an entire Pro-session btw. - and documented the results in this pr: https://github.com/google-gemini/gemini-cli/pull/26129 - I am using the optimized prompt and had no issues so far I had with the original one.

Another big issue is "model panic" - the Gemini model often overwrites entire files "from memory" with write_file, when the edit tool fails 2 times. That occasionally causes code degradation, and when there are multiple turns and there is no backup, the model starts to get nuts, trying to fix its errors, and destroys even more in the effort to just make it compile / build. I gave it a rotating pre-write backup and a restore_file tool. The model panic vanished. https://github.com/google-gemini/gemini-cli/pull/25947

With these two fixes, Gemini CLI is pretty good - without them I wouldn't use it.

Why oklch

HSL looks perceptually uniform but isn’t. An hsl(60, 100%, 50%) yellow and an hsl(240, 100%, 50%) blue share the same L value (50%), but the yellow looks dramatically lighter. If you try to build a lightness scale with HSL, you end up hand-tuning every hue to compensate.

oklch isn’t harder to read than hsl once you internalise three numbers: L (how bright, 0–1), C (how vivid, 0–0.4), H (which colour, 0–360). And it gives you something hsl can’t: the guarantee that your lightness scale actually looks even.

100 Useful Command-Line Utilities
A Guide to 100 (ish) Useful Commands

Design with the screen reader layer in mind

Closing the gap between what you’ve designed and what’s announced by screen readers doesn’t require accessibility or production code expertise. It’s about being more intentional with the decisions you’re already making.

Though screen reader output depends on how the design was developed, designers have more influence over the screen reader experience than they think.

Think “role, name, state” while designing

You don’t need to memorize ARIA specs to improve accessibility. Just ask yourself three questions as you design elements like interactive or informative elements:

Role: What is this?
Name: What does it do?
State: What’s its current condition?

Learn how DNS works: hierarchy, records (A, CNAME, MX, TXT), TTL caching, and the full resolution process. Plus commands to view and clear DNS cache.

#291 1898 50¢ Trans-Mississippi

Basic Information
Color: Sage Green
Subject: Western Mining Prospector
Watermark: Watermarked double-lined USPS
Paper: Soft porous paper
Printing method: line-engraved intaglio on flat plates
Perforations: 12
Scott #: 291
Quantity issued: 530,400
Issued: June 17th, 1898
Value
An unused stamp with perfect gum: $375-$650
An unused stamp with gum and a hinge mark: $90-$175
A used stamp: $19-$30

Build an elegant set of poker chip trays!

A new immersive art project in Los Angeles is set to occupy the shuttered St. Vincent Medical Center, turning a site in transition into a large-scale environment organized around human emotion.