#learninpublic — Public Fediverse posts

self-taught dev grinding through a 24-week roadmap — currently deep in
CSS (specificity, inheritance, the cascade). building guerz.lol in
public as my homework hub + portfolio.

i also stream the actual learning, IRL — roadmap + site + stream link
all in my bio if you wanna watch someone figure it out in real time,
ugly drafts and all.

#webdev #css #learninpublic #100DaysOfCode

I heard it's extremely hard to get printers working on Linux. Perhaps I was just lucky but with my old Canon MG2400 it just worked. AUR driver package certainly helped. #linux #learninpublic

I heard it's extremely hard to get printers working on Linux. Perhaps I was just lucky but with my old Canon MG2400 it just worked. AUR driver package certainly helped. #linux #learninpublic

Day 87 of learning AI/ML

I studied Inference for regression slope

• Inference about slope (linear regression)
• Conditions for valid inference
• Confidence interval for slope
• t-statistic for slope
• Using p-value to conclude

#LearnInPublic #AI #ML

Day 87 of learning AI/ML

I studied Inference for regression slope

• Inference about slope (linear regression)
• Conditions for valid inference
• Confidence interval for slope
• t-statistic for slope
• Using p-value to conclude

#LearnInPublic #AI #ML

Day 86 of learning AI/ML

I studied Chi-square tests (tables & association)

• Frequency & contingency tables
• Chi-square test for homogeneity
• Chi-square test for independence
• Testing relationships between variables

#LearnInPublic #AI #ML

Day 86 of learning AI/ML

I studied Chi-square tests (tables & association)

• Frequency & contingency tables
• Chi-square test for homogeneity
• Chi-square test for independence
• Testing relationships between variables

#LearnInPublic #AI #ML

Day 85 of learning AI/ML

I studied Chi-square tests (categorical data)

• Inference for categorical data
• Chi-square distribution (intro)
• Goodness-of-fit test
• Chi-square statistic
• Interpreting results

#LearnInPublic #AI #ML

Day 85 of learning AI/ML

I studied Chi-square tests (categorical data)

• Inference for categorical data
• Chi-square distribution (intro)
• Goodness-of-fit test
• Chi-square statistic
• Interpreting results

#LearnInPublic #AI #ML

Day 84 of learning AI/ML

I studied Comparing means

• Statistical significance (real example)
• Difference of sample means distribution
• Confidence interval for difference of means
• Hypothesis test for difference of means

#LearnInPublic #AI #ML

Day 84 of learning AI/ML

I studied Comparing means

• Statistical significance (real example)
• Difference of sample means distribution
• Confidence interval for difference of means
• Hypothesis test for difference of means

#LearnInPublic #AI #ML

Day 83 of learning AI/ML

I studied Comparing population proportions

• Comparing two population proportions
• Hypothesis testing for proportions
• Interpreting statistical significance
• Drawing conclusions from experiments

#LearnInPublic #AI #ML

Day 83 of learning AI/ML

I studied Comparing population proportions

• Comparing two population proportions
• Hypothesis testing for proportions
• Interpreting statistical significance
• Drawing conclusions from experiments

#LearnInPublic #AI #ML

Day 82 of learning AI/ML

I studied Hypothesis testing (summary)

• Hypothesis testing & p-values
• One-tailed vs two-tailed tests
• z vs t statistics
• Small vs large sample tests
• Proportion hypothesis testing

#LearnInPublic #AI #ML

Day 82 of learning AI/ML

I studied Hypothesis testing (summary)

• Hypothesis testing & p-values
• One-tailed vs two-tailed tests
• z vs t statistics
• Small vs large sample tests
• Proportion hypothesis testing

#LearnInPublic #AI #ML

Day 81 of learning AI/ML

I studied Hypothesis testing for a mean

• Writing hypotheses (mean)
• Conditions for t-test
• When to use z vs t
• Calculating t-statistic
• Finding & comparing p-values
• Making conclusions from test

#LearnInPublic #AI #ml

Day 81 of learning AI/ML

I studied Hypothesis testing for a mean

• Writing hypotheses (mean)
• Conditions for t-test
• When to use z vs t
• Calculating t-statistic
• Finding & comparing p-values
• Making conclusions from test

#LearnInPublic #AI #ml

Day 80 of learning AI/ML

I studied Hypothesis testing for proportions

• Constructing null & alternative hypotheses
• Conditions for z-test (proportion)
• Calculating p-value from z-score
• Making conclusions from test results

#LearnInPublic #AI #ML

Day 80 of learning AI/ML

I studied Hypothesis testing for proportions

• Constructing null & alternative hypotheses
• Conditions for z-test (proportion)
• Calculating p-value from z-score
• Making conclusions from test results

#LearnInPublic #AI #ML

Day 79 of learning AI/ML

I studied Hypothesis testing (errors & power)

• Type I error, type II error (false negative)
• Power of a test (detecting true effect)
• Trade-off between errors & significance
• Real-world consequences of decisions

#LearnInPublic #AI #ML

Day 79 of learning AI/ML

I studied Hypothesis testing (errors & power)

• Type I error, type II error (false negative)
• Power of a test (detecting true effect)
• Trade-off between errors & significance
• Real-world consequences of decisions

#LearnInPublic #AI #ML

Day 78 of learning AI/ML

I studied Hypothesis testing
@khanacademy Unit 12

• Idea behind hypothesis testing
• Null vs alternative hypothesis
• p-values & significance levels
• Estimating p-values (simulation)
• Using p-values to draw conclusions

#LearnInPublic #AI #ML

Day 78 of learning AI/ML

I studied Hypothesis testing
@khanacademy Unit 12

• Idea behind hypothesis testing
• Null vs alternative hypothesis
• p-values & significance levels
• Estimating p-values (simulation)
• Using p-values to draw conclusions

#LearnInPublic #AI #ML

Day 76 of learning AI/ML

I studied t-intervals for mean

• Constructing t-interval for a mean
• Paired data confidence intervals
• Interpreting confidence intervals
• Sample size vs margin of error
• Small sample t-intervals

#LearnInPublic #AI #ML

Day 76 of learning AI/ML

I studied t-intervals for mean

• Constructing t-interval for a mean
• Paired data confidence intervals
• Interpreting confidence intervals
• Sample size vs margin of error
• Small sample t-intervals

#LearnInPublic #AI #ML

Day 75 of learning AI/ML

I studied t-distribution & inference
@khanacademy Unit 11

• Intro to t-statistics
• Why t is used (simulation insight)
• Conditions for valid t-intervals
• Inference on a mean
• Finding critical t-values

#LearnInPublic #AI #ML

Day 75 of learning AI/ML

I studied t-distribution & inference
@khanacademy Unit 11

• Intro to t-statistics
• Why t is used (simulation insight)
• Conditions for valid t-intervals
• Inference on a mean
• Finding critical t-values

#LearnInPublic #AI #ML

Day 74 of learning AI/ML

I studied Confidence intervals for proportions

• Margin of error & examples
• Conditions for valid CI (proportion)
• Critical value & confidence level
• Constructing & interpreting z-interval
• Sample size vs margin of error

#LearnInPublic #AI #ML

Day 74 of learning AI/ML

I studied Confidence intervals for proportions

• Margin of error & examples
• Conditions for valid CI (proportion)
• Critical value & confidence level
• Constructing & interpreting z-interval
• Sample size vs margin of error

#LearnInPublic #AI #ML

Day 73 of learning AI/ML

I studied Confidence intervals
@khanacademy Unit 11

• Confidence intervals & margin of error
• Confidence interval simulation
• Interpreting confidence levels
• Understanding what confidence intervals mean

#LearnInPublic #AI #ML

Day 73 of learning AI/ML

I studied Confidence intervals
@khanacademy Unit 11

• Confidence intervals & margin of error
• Confidence interval simulation
• Interpreting confidence levels
• Understanding what confidence intervals mean

#LearnInPublic #AI #ML

Day 72 of learning AI/ML

I studied
Sampling distribution of sample mean

• Inferring population mean from sample mean
• Central Limit Theorem (CLT)
• Standard error of the mean
• Mean & std of sample means
• Finding probabilities using sample means

#LearnInPublic #AI #ML

Day 72 of learning AI/ML

I studied
Sampling distribution of sample mean

• Inferring population mean from sample mean
• Central Limit Theorem (CLT)
• Standard error of the mean
• Mean & std of sample means
• Finding probabilities using sample means

#LearnInPublic #AI #ML

Day 71 of learning AI/ML

I studied Sampling distribution of proportions

• Sampling distribution of sample proportion
• Conditions for normal approximation
• Mean & standard deviation of proportions
• Finding probabilities using sample proportions

#LearnInPublic #AI #ML

Day 71 of learning AI/ML

I studied Sampling distribution of proportions

• Sampling distribution of sample proportion
• Conditions for normal approximation
• Mean & standard deviation of proportions
• Finding probabilities using sample proportions

#LearnInPublic #AI #ML

Try a 4-person micro-cohort this month.
• Rule: one short ask + one tiny test each week.
• Rotate who gets focused feedback; everyone does one action and reports 48 hours later.
• Optional: pool a token amount to fund the clearest next test.
Small groups surface clearer actions, faster learning. Who would you invite to a four-person cohort? 🤝📈 #learninpublic #microtests

Heute mal wieder stundenlang ein Open-Source-Tool konfiguriert, das eigentlich "einfach funktionieren" sollte. Aber weißt du was? Ich hab dabei mehr gelernt als in jedem Tutorial. 🔧

Das ist irgendwie das Schöne daran: Der Weg durch die Config-Hölle IST die Dokumentation.

(Der Kaffee war trotzdem alle.) ☕

#OpenSource #Linux #SysAdmin #LearnInPublic

Heute mal wieder stundenlang ein Open-Source-Tool konfiguriert, das eigentlich "einfach funktionieren" sollte. Aber weißt du was? Ich hab dabei mehr gelernt als in jedem Tutorial. 🔧

Das ist irgendwie das Schöne daran: Der Weg durch die Config-Hölle IST die Dokumentation.

(Der Kaffee war trotzdem alle.) ☕

#OpenSource #Linux #SysAdmin #LearnInPublic

Future Outlook

AI will become a foundational utility like electricity, integrating deeply with physical reality. The next phase requires high-efficiency architectures and seamless interaction. Ultimate scientific breakthroughs will depend on overcoming bottlenecks in energy, materials, safety, and human constraints.

#LearnInPublic #AI

Recently I've been thinking a lot about early optimization and over engineered. Maybe I just need to embrace the chaos. Instead of making everything fine, I should try to make everything works.

#LearnInPublic #MentalModel

The essential difference is that compiled languages require a full build to run, while interpreted languages offer instant feedback and higher modifiability. This feels less like a binary choice and more like a deliberate design pattern.

#LearnInPublic #Compiler #Python #ComputerScience #ProgrammingLanguage

https://www.youtube.com/watch?si=gewGIpBpGfZ_SOvf&v=RnBOOF502p0&feature=youtu.be (2/2)

Next I can tell Claude Code to send me notifications via ntfy anytime that it finishes or needs input.

https://dandylyons.net/notes/claude+ntfy

#ntfy #ClaudeCode #LearnInPublic

Next I can tell Claude Code to send me notifications via ntfy anytime that it finishes or needs input.

https://dandylyons.net/notes/claude+ntfy

#ntfy #ClaudeCode #LearnInPublic

Today I set up a ntfy server. Now I can send notifications from the command line to myself on my iPhone, Mac, or browser.

https://ntfy.sh

#ntfy #learninpublic

Day 40 of #100DaysOfCode

Built a memory game from a JavaScript tutorial video on freeCodeCamp

#JavaScript #LearnInpublic

Day 39 of #100DaysOfCode

I made an arcade style Rock, Paper, Scissors Game using HTML, CSS & JavaScript.

#JavaScript #LearnInPublic

Day 38 of #100DaysOfCode

Started reading up on JavaScript Regular Expressions.
Didn't get too far.

#JavaScript #LearnInPublic

Day 37 of #100DaysOfCode

Learnt about JavaScript Maps
- Map Methods
- JavaScript WeakMaps
- WeakMap Methods
- Math Objects

#JavaScript #LearnInPublic