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Annual Production of 1/72 (22mm) scale plastic soldiers, 1958-2025

https://plasticsoldierreview.com/ShowFeature.aspx?id=27
1•YeGoblynQueenne•34s ago•0 comments

Error-Handling and Locality

https://www.natemeyvis.com/error-handling-and-locality/
1•Theaetetus•1m ago•0 comments

Petition for David Sacks to Self-Deport

https://form.jotform.com/253464131055147
1•resters•2m ago•0 comments

Get found where people search today

https://kleonotus.com/
1•makenotesfast•4m ago•1 comments

Show HN: An early-warning system for SaaS churn (not another dashboard)

https://firstdistro.com
1•Jide_Lambo•5m ago•0 comments

Tell HN: Musk has never *tweeted* a guess for real identity of Satoshi Nakamoto

1•tokenmemory•5m ago•0 comments

A Practical Approach to Verifying Code at Scale

https://alignment.openai.com/scaling-code-verification/
1•gmays•7m ago•0 comments

Show HN: macOS tool to restore window layouts

https://github.com/zembutsu/tsubame
1•zembutsu•10m ago•0 comments

30 Years of <Br> Tags

https://www.artmann.co/articles/30-years-of-br-tags
1•FragrantRiver•16m ago•0 comments

Kyoto

https://github.com/stevepeak/kyoto
2•handfuloflight•17m ago•0 comments

Decision Support System for Wind Farm Maintenance Using Robotic Agents

https://www.mdpi.com/2571-5577/8/6/190
1•PaulHoule•18m ago•0 comments

Show HN: X-AnyLabeling – An open-source multimodal annotation ecosystem for CV

https://github.com/CVHub520/X-AnyLabeling
1•CVHub520•20m ago•0 comments

Penpot Docker Extension

https://www.ajeetraina.com/introducing-the-penpot-docker-extension-one-click-deployment-for-self-...
1•rainasajeet•21m ago•0 comments

Company Thinks It Can Power AI Data Centers with Supersonic Jet Engines

https://www.extremetech.com/science/this-company-thinks-it-can-power-ai-data-centers-with-superso...
1•vanburen•24m ago•0 comments

If AIs can feel pain, what is our responsibility towards them?

https://aeon.co/essays/if-ais-can-feel-pain-what-is-our-responsibility-towards-them
3•rwmj•28m ago•5 comments

Elon Musk's xAI Sues Apple and OpenAI over App Store Drama

https://mashable.com/article/elon-musk-xai-lawsuit-apple-openai
1•paulatreides•31m ago•1 comments

Ask HN: Build it yourself SWE blogs?

1•bawis•31m ago•1 comments

Original Apollo 11 Guidance Computer source code

https://github.com/chrislgarry/Apollo-11
3•Fiveplus•37m ago•0 comments

How Did the CIA Lose Nuclear Device?

https://www.nytimes.com/interactive/2025/12/13/world/asia/cia-nuclear-device-himalayas-nanda-devi...
1•Wonnk13•37m ago•0 comments

Is vibe coding the new gateway to technical debt?

https://www.infoworld.com/article/4098925/is-vibe-coding-the-new-gateway-to-technical-debt.html
1•birdculture•41m ago•1 comments

Why Rust for Embedded Systems? (and Why I'm Teaching Robotics with It)

https://blog.ravven.dev/blog/why-rust-for-embedded-systems/
2•aeyonblack•43m ago•0 comments

EU: Protecting children without the privacy nightmare of Digital IDs

https://democrats.eu/en/protecting-minors-online-without-violating-privacy-is-possible/
3•valkrieco•43m ago•0 comments

Using E2E Tests as Documentation

https://www.vaslabs.io/post/using-e2e-tests-as-documentation
1•lihaoyi•44m ago•0 comments

Apple Welcome Screen: iWeb

https://www.apple.com/welcomescreen/ilife/iweb-3/
1•hackerbeat•45m ago•1 comments

Accessible Perceptual Contrast Algorithm (APCA) in a Nutshell

https://git.apcacontrast.com/documentation/APCA_in_a_Nutshell.html
1•Kerrick•46m ago•0 comments

AI agent finds more security flaws than human hackers at Stanford

https://scienceclock.com/ai-agent-beats-human-hackers-in-stanford-cybersecurity-experiment/
3•ashishgupta2209•48m ago•2 comments

Nano banana prompts, updates everyday

https://github.com/fionalee1412/bestnanobananaprompt-github
4•AI_kid1412•51m ago•0 comments

Skills vs. Dynamic MCP Loadouts

https://lucumr.pocoo.org/2025/12/13/skills-vs-mcp/
3•cube2222•55m ago•0 comments

Top validated AI-SaaS Ideas are available here

1•peterbricks•59m ago•0 comments

UnmaskIP: A Clean, Ad-Free IP and Deep Packet Leak Checker

https://unmaskip.net
1•kfwkwefwef•1h ago•0 comments
Open in hackernews

Estimating Logarithms

https://obrhubr.org/logarithm-estimation
103•surprisetalk•6mo ago

Comments

xeonmc•6mo ago
Protip: since halving and doubling are the same logarithmic distance on either sides of unity, and the logarithmic distance of 2.0 to 5.0 is just a tiny bit larger than that of doubling, this means that you can roughly eyeball the infra-decade fraction by cutting them into thirds
thechao•6mo ago
I don't know about powers-of-10; but, you can use something similar to bootstrap logs-in-your-head.

So, 2^10=1024. That means log10(2)~3/10=0.3. By log laws: 1 - .3 = 0.7 ~ log10(5).

Similarly, log10(3)*9 ~ 4 + log10(2); so, log10(3) ~ .477.

Other prime numbers use similar "easy power rules".

Now, what's log10(80)? It's .3*3 + 1 ~ 1.9. (The real value is 1.903...).

The log10(75) ~ .7*2+.477 = 1.877 (the real answer is 1.875...).

Just knowing some basic "small prime" logs lets you rapidly calculate logs in your head.

madcaptenor•6mo ago
For log(3) I prefer the "musical" approximation 2^19 ~ 3^12. This is a "musical" fact because it translates into 2^(7/12) ~ 3/2 - that is, seven semitones make a perfect fifth). Together with log(2) ~ 3/10 that gives log(3) ~ 19/40.

Also easy to remember: 7^4 = 2401 ~ 2400. log(2400) = log(3) + 3 log(2) + 2 ~ 19/40 + 3 * 12/40 + 2 = 135/40, so you get log(7) ~ 135/160 = 27/32 = 0.84375.

thechao•6mo ago
These are both great! I learned most of these old tricks from my dad & grandfather.
thechao•6mo ago
I'll double-reply, since I think people might also appreciate this... there's a pretty easy way to find the square of 2-leading-digit numbers (and square-roots, too.)

Direct square: 54^2 = (50 + 4)^2 = 2500 + 2 * 50 * 4 + 4^2.

Direct square-root: 3800 = (61^2=3721) + r ~ (61 + (3800-3721)/(2*61) = 61 + 79/122. To check: (61 + 79/122)^2= 3,800.419. You can estimate the "overhead" by noting that 79/122 ~ 2/3 => (2/3)^2 = 0.444.

Of course, my grandfather would've just used a slide-rule directly.

briian•6mo ago
So much of economics maths/stats is built on this one little trick.

It's still pretty cool to me that A this works and B it can be used to do so much.

saulpw•6mo ago
Here's all you really need to know about logs when estimating in your head:

The number of digits minus one is the magnitude (integer). Then add the leading digit like so:

1x = ^0.0

2x = ^0.3 (actually ^0.301...)

pi = ^0.5 (actually ^0.497...)

5x = ^0.7 (actually ^0.699...)

Between these, you can interpolate linearly and it's fine for estimating. Also 3x is close enough to pi to also be considered ^0.5.

In fact, if all you're doing is estimating, you don't even really need to know the above log table. Just use the first digit of the original number as the first digit past the decimal. So like 6000 would be ^3.6 (whereas it's actually ^3.78). It's "wrong" but not that far off if you're using logarithmetic for napkin math.

xeonmc•6mo ago
And this is also the basis of the fast inverse square root algorithm. Floating point numbers are just linear interpolations between octaves.
thomasahle•6mo ago
What is this ^notation?

Looks like 5x=^0.699 means log_10(5)=0.699.

xeonmc•6mo ago
5 = 10^0.699
saulpw•6mo ago
It's magnitude notation. ^X is short for 10^X.
dhosek•6mo ago
The pi = ^0.5 bit reminds me of a college physics professor who was fond of using the shortcut π²=10.
madcaptenor•6mo ago
This is wrong, π² = g, the acceleration due to gravity.
brucehoult•6mo ago
Slightly more accurate ..

    1: 0.0
    2: 0.3
    3: 0.475
    4: 0.6 (2*2)
    5: 0.7
    6: 0.775 (2*3)
    7: 0.85
    8: 0.9 (2*2*2)
    9: 0.95 (3*3)
The biggest error is 7 at +0.577% ... 0.845 is almost perfect. The others are maximum +0.45% off.

So you only need to remember:

    2: 0.3
    7: 0.85
    9: 0.95

    1.4 = sqrt(2) -> 0.15
    3 = sqrt(9) -> 0.95/2 = 0.475
    pi = ~sqrt(10) -> 0.5
    4 = 2*2 -> 0.6
    5 = 10/2 -> 0.7
    6 = 2*3
    2pi = 2*sqrt(10) -> 0.3+0.5 = 0.8
    8 = 2*2*2 -> 0.9
madcaptenor•6mo ago
You barely even have to remember

  9: 0.95
since you can get it by interpolation between 8 and 10.
brucehoult•6mo ago
Yup.

Put the effort into remembering a 3 digit log for 7 instead?

Or keep the same precision with ...

    7 = ~sqrt(100/2) -> (2-0.3)/2 = 1.7/2 = 0.85
Log 2 is all you need?

Or even ...

    7 = sqrt(sqrt(2401)) = (3*8*100)^(1/4) -> 0.95/8 + 0.3*3/4 + 2/4 = 0.12 + 0.225 + 0.5 = 0.845
VERY accurate, but that's getting to be too much.
arkeros•6mo ago
You also don't need to remember 7, as 2*7^2 =~ 100 => log 7 =~ (2 - 0.3) / 2 = 0.85
brucehoult•6mo ago
Yes, as I mentioned in one of my other replies. Or 2400^0.25.

Same for 9: sqrt(81) = sqrt(8 * 10) -> (0.3*3 + 1) / 2 = 0.95

At some point it's easier to remember a few numbers than calculate a lot of formulas.

adrian_b•6mo ago
I find much more useful to memorize the inverse of that table, i.e. the decibel table.

The numbers corresponding approximately to 0 dB, 1 dB, 2 dB, 3 dB etc. (1.0, 1.25, 1.6, 2.0 etc.; more accurate values, like 1.26 or 1.58 instead of 1.25 and 1.6 are typically not needed) are also frequently encountered as such in engineering practice as belonging to various series of standard nominal values, which makes them more useful to have in mind.

Then for any mental computation, it is trivial to convert mentally a given number to the corresponding dB value, perform the computation with additions and subtractions instead of multiplications and divisions, then convert back the dB value to the linear number corresponding to the result.

Doing mental computations in this way, even if it has only about 2 correct digits at most, is enough for debugging various hardware problems, or even for catching software bugs, where frequently an impressive numbers of significant digits is displayed, but the order of magnitude can be completely wrong.

madcaptenor•6mo ago
Since 3 dB is so nearly 2.0, you can derive that series by taking small powers of two, say from 2^(-4) to 2^5 or 2^6:

0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, (64)

and then multiplying everything by factors of 10 to get it between 0 and 10:

1, 1.25, 1.6, 2, 2.5, 3.2, 4, 5, 6.25 (or 6.4), 8

adrian_b•6mo ago
You are right, so this is how the series of standardized nominal values that has been traditionally used for some quantities, like maximum dissipated powers for resistors, maximum operating voltages for capacitors, maximum powers for motors, hexagonal nut sizes for some ancient packages for stud-mounted power semiconductor devices, etc., has been obtained.

Apparently whoever have standardized first this series of values have not been able to make their mind to choose between 6.25 and 6.4, therefore 6.3 has been chosen instead of any of those 2 values.

gus_massa•6mo ago
Typo near the top, in case someone knows the author:

> log(100)≤log(N)<log(100)

There is a missing 0 in the last log. It should be

> log(100)≤log(N)<log(1000)

obrhubr•6mo ago
Thanks for pointing this out :), I fixed it!
stevefan1999•6mo ago
Related Wikipedia entry: https://en.wikipedia.org/wiki/Logarithmic_number_system

Also related: https://blog.timhutt.co.uk/fast-inverse-square-root/

(I see that someone already mentioned fast inverse square root algorithm is related to this, which is famously used by John Carmack which is one of my hero who led me into tech industry, despite I didn't end up in gaming industry)

teo_zero•6mo ago
The artcle makes a weird use of the notation for successive exponentiations. It's difficult to represent the concept here as only text is allowed, but shortly the author uses a^b^c to mean (a^b)^c. This is counter intuitive as in most mathematical contexts exponentiation is right-associative, that is a^b^c means a^(b^c). A pair of parentheses would remove any doubts!