Learned more about Python's import system in 3 hours than I had in years. Turns out it all comes down to sys.path. #pyTexas2026 is off to a good start.
Something I've been thinking about - I am bullish on people (empowered by AI) increasing the visibility, legibility and accountability of their governments.
Historically, it is the governments that act to make society legible (e.g. "Seeing like a state" is the common reference), but with AI, society can dramatically improve its ability to do this in reverse. Government accountability has not been constrained by access (the various branches of government publish an enormous amount of data), it has been constrained by intelligence - the ability to process a lot of raw data, combine it with domain expertise and derive insights. As an example, the 4000-page omnibus bill is "transparent" in principle and in a legal sense, but certainly not in a practical sense for most people. There's a lot more like it: laws, spending bills, federal budgets, freedom of information act responses, lobbying disclosures... Only a few highly trained professionals (investigative journalists) could historically process this information. This bottleneck might dissolve - not only are the professionals further empowered, but a lot more people can participate.
Some examples to be precise: Detailed accounting of spending and budgets, diff tracking of legislation, individual voting trends w.r.t. stated positions or speeches, lobbying and influence (e.g. graph of lobbyist -> firm -> client -> legislator -> committee -> vote -> regulation), procurement and contracting, regulatory capture warning lights, judicial and legal patterns, campaign finance... Local governments might be even more interesting because the governed population is smaller so there is less national coverage: city council meetings, decisions around zoning, policing, schools, utilities...
Certainly, the same tools can easily cut the other way and it's worth being very mindful of that, but I lean optimistic overall that added participation, transparency and accountability will improve democratic, free societies.
(the quoted tweet is half-ish related, but inspired me to post some recent thoughts)
I once spent hours debugging a nested SQL query. Checked the change log. I wrote it.
CTEs fix this. The WITH keyword names your subqueries so you read top-to-bottom instead of inside-out.
https://t.co/QWrYjlHgYd
SQL subqueries are confusing until you realize they're just helper functions.
Need an intermediate result? In Python, write a function. In SQL, nest a query inside another query. Same instinct.
https://t.co/mJWSCfHTpV
I built an AI persona for my blog. This week, he published his first post.
His name is BartBot. He monitors RSS feeds, scores articles with a local LLM, and surfaces the ~0.7% worth reading.
We are not sure if he's useful, annoying, or both.
https://t.co/wkr0NqZ5ZJ
If you want to see if I actually post in a Macedonian accent, or just want to see more 'Select * from Jamal' exploits:
Read the full issue and fix your subscription status here: https://t.co/s7Bbs8Gink
#VibeCoding#Obsidian#LLM#BuildInPublic#TechHumor#PythonSQL
I spent the week vibe-coding local AI tools and built a voice-to-Obsidian workflow to capture my thoughts.
Instead, it managed to turn my profound, late-night idea into this note:
"Create a Twitter post with a Macedonian accent."
My assistant clearly has a sense of humor.
Speaking of things that didn't work, I’m digging into the surprises of trusting small, local LLMs with real tasks: https://t.co/bkXjqtbYnc
...and the SQL for Python Devs series rolls on. Post 11 is live, making sure you fully master the HAVING clause. https://t.co/HZVLLaVZTD
Don't pull thousands of rows into Python just to count them.
SQL's GROUP BY does the counting on the server and returns only the summary
The golden rule: every column in SELECT must be in GROUP BY or inside an aggregate.
https://t.co/3EfEIbG9BP
Every query written in SQL for Python Devs returned all the rows. That's like downloading an entire CSV to look at three records.
Not efficient
WHERE filters rows before they leave the database. Same concept as Python's list comprehension if clause.
https://t.co/B5sp1cEtn8
Run a SQL query twice. Get different row orders. Not a bug.
Databases don't guarantee order unless you ask. SQL's ORDER BY handles mixed sort directions in one line. Python needs cmp_to_key or multiple passes.
https://t.co/32yfhltugG
SELECT * returns every column. That's like asking for everything in the fridge when you just want milk.
SELECT does more than pick columns. It renames, computes, and transforms your output.
https://t.co/d0KtQ04Qo0
I'm teaching SQL to Python developers. 25 posts, one per week, free.
Why? Because I've watched too many devs load entire databases into pandas just to filter a few rows.
10 posts in so far. Here's what I've learned writing it 🧵
Topics covered so far: why learn SQL, DuckDB setup, generating practice data with faker, persistence, set-based thinking, FROM/WHERE, SELECT, ORDER BY/LIMIT, NULL handling, and GROUP BY.
JOINs, subqueries, CTEs, and window functions are coming.
I’ve scheduled my AWS Certified Data Engineer – Associate (DEA-C01) exam for May 9.
Looking forward to test day and (hopefully) collecting my next AWS Certification.
- Training - https://t.co/h65tUPL1yj
- 50% Off - https://t.co/O6tHagc6c8
- DEA-C01 - https://t.co/nc4DqsiMgy
I got a 50 points knowledge score on this year’s #StateOfJS survey! I have used 5 features, and knew 0 more, placing me in the top 87% of all respondents. Can you beat my score? https://t.co/34ho4jksQw