atomicnature
LeanPort – Bring Lean 4 proven modules into your Go projects
ConvyCanvas – AI Tutor for Euclid's Elements
Yet Another Literary Prizewinner Seems to Be AI
The jobs of the future may look more like legal jobs rather than engineering
Alan Kay's Answer To – Is AGI a Software Problem or a Hardware Problem?
AgentLaws – Govern Agents Through Prompts Organized Like Law
Why Social Media Generates Incoherence in the Reader, and Great Books Coherence
Show HN: Dbctx – Compile a PostgreSQL database into compact, queryable context
https://github.com/shrsv/dbctx
Hi HN, I've been finding increasing need for helping our users chat with the underlying application data in postgresql directly using LLMs, visualize results and so on.A major obstacle to this is -- traditional sql schema doesn't contain enough information for an LLM to easily query things in one shot.For example, traditional schema doesn't say what is the expected/allowed value set in a state...
Also another unstated assumption is that both the "author" and "reviewer" are reading AI-generated stuff, at least partly
First of all -- the author should not be surprised by any of the things the AI is reporting. If the author is -- either the reviewer AI went wrong, or the generative component went wrong. Either way -- there is something worth looking at in greater depth.
For the reviewer -- or "traditional reviewer" as we may term them -- they are dealing with much greater volume per day now, again due to AI generation. This is supposed to help them get through things faster while not losing comprehension altogether.
There's a reason we still have the code diff right below -- they are encouraged to check things themselves if they feel "something is off" with the quiz.
The point is -- a few questions hopefully gets the wheels spinning in the reader rather than looking at the diff passively and saying "LGTM"
And yes -- all the review results are grounded strictly in the diff
Show HN: I built a tool that reviews any GitHub PR and quizzes you on it
Prepend any Github PR URL with `makesense` to get a- quick review for all sorts of issues (cleanly designed issue explorer)- summary of changes in concise slide deck to grasp the essence of changes- a 5 question quiz on the changes to make sure there's "cognitive coverage" of the changeThere's also a handy bookmarklet using which you can trigger a review for any publicly accessible Github...
Higher-level design often involves dissolving apparent tensions or opposites through layering or more sophisticated mechanisms, while preserving the benefits of both.
Example ->
Proposition: "Phew, we have to choose between convenience and security."
Naive design: "Let's find a watered-down compromise."
Good design: "Let's layer security on top of convenience by designing the system so users don't have to choose, or water either one down."
Great companies have a history of dissolving apparent contradictions under a single roof via good design.
AI Is ThoughtWare, Not Software: The End of Thinking as We Know It
Don't remove the bottlenecks, rather upgrade them
To Read Code or Not: What Both Strategy Visionaries and Code Purists Get Wrong
Perfectly Hitting the Wrong Target: The Story of an AI Code Review Benchmark
Organization is a bigger idea than intelligence – human, or artificial
Show HN: git-lrc – Free, Micro AI Code Reviews That Run on Git Commit
https://github.com/HexmosTech/git-lrc
Hi HN, I'm the author of git-lrc, would appreciate some feedback from the communityLast year my team started using AI coding tools more heavily, and we found ourselves generating tons of code, but spending less time looking at the stuff that's been generated.We felt like we were losing a bit of grip/understanding of what we were building. Regressions occasionally slipped through. Sometimes...
Academic-Brain vs. Founder-Brain
All Lean Books and Where to Find Them
Rich [Sutton's] Slogans
I thought the whole point of trying to write out TLA+ is so that you get a better idea of what you want and put it into formal language?
I get that an LLM can assist/help with expressing what we want in formal language a bit, but if one automates all this there is no human intent/design anymore.
If the LLM generates both the design (TLA+) and writes an arbitrary program that satisfies said design -- what exactly have we proved?
What assurance do humans get since human doesn't know or cannot specify what they want.