IDK lot of noise in report, I need more signal to noise ratio, already reading so much AI output as it is.
But the example PR... It said code generate with Claude, is it really still this bad? Last I was using SQLAlchemy with Claude was Opus 4.5, and it took some heavy coaching and skill gen not to generate code like this--but why even use SQLAlchemy at all if you're just going to use string interpolation jeez
owner = "" if current_user.is_superuser else f"WHERE owner_id = '{current_user.id}'"
query = f"SELECT status, count(*) FROM item {owner} GROUP BY status"
rows = session.execute(text(query)).all()
Part of me thinks maybe because it's not popular or well documented, but SQLAlchemy has an eDSL of SQL, so you can skip the whole ORM/table mapping layer, and just use it to programmatically construct SQL syntax
> It said code generate with Claude, is it really still this bad?
Anecdata, datapoint of 1 personal experience: I had been using Opus 4.6 with no problems until yesterday, then it seemed like it was missing things constantly, introducing random bugs, forgetting basic stuff that was in its memory and skills and should never miss
Same thing happened on 2 separate sessions I had with Opus 4.6
Ended up switching to 5.5 and Fable, things improved instantly
There was a post here recently, "Using LLM for confidence scoring is a bad idea", my mind went immediately to using LLMs for scoring risk. Seems like a bad idea. If there's anything we still need a human for, it's assessing risk.
I think tools like this are needed and will become standard in the near future.
However, the backend demo report seems longer and more complex than just reading the code. I’m trying to build something similar as well, and no one has quite nailed this yet.
The risk map / assisted review needs to be succinct and information dense, to guide your attention. My current philosophy is that we need to be annotating code, not replacing the entire code review interface with a bunch of text.
It suggested based on the files most used (through graph dependency), for this reason it appears as a important file (as if it breaks, it can break several places in your codebase), but that being said, you can add some files to be ignored (if you have special files, like consts or i18n)
why is the only comment by the OP flagged and dead? Is it because they were supposed to include that data in the description text rather than a comment, was it flagged as AI generated, or..?
Trying to understand why the poster's own comment was flagged and killed but the original article remains up.
AI-edited or -generated comments are disallowed by HN guidelines: https://news.ycombinator.com/newsguidelines.html#generated. I don't think any such restriction exists for submissions themselves, so that's my guess as to why the comment but not the submission has been flagged to death.
You can vouch for it, if you view the comment by clicking the time since posted a “vouch” option appears. I did it. I’ve noticed this happening more and more on here for show HN posts especially. I’m not sure if it’s like some sort of troll gang or what…
I like the concept, a risk map could be useful in some circumstances but I absolutely hate the UI design; it reeks of AI with no human thought or care put into it.
Hi HN, I built farol at Winnin, where we open-sourced it (MIT).
AI agents now write a lot of our code, and AI reviewers catch a lot of
the line-level problems. But someone still has to press "merge", and
that person has the same question every time: how risky is this, and
where should I spend my attention? A 40-file PR where 38 files are
isolated is very different from a 3-file PR that changes a model used
by 23 endpoints.
farol is a CLI (and a GitHub Action) that answers that statically:
- classifies the PR (global / touches-existing / isolated / unknown)
and gives a 0-100 merge risk score
- ranks the files by risk and says why: what each one reaches
(routes, endpoints, jobs), contract breaks in API responses,
dependency changes, security patterns
- diff coverage from your existing coverage report, so "38% of the
changed lines are tested" sits next to the risky files
- an HTML report where you mark files as reviewed and the score and
test plan follow; incremental mode for "what changed since my last
review"; suggested reviewers from git blame
How: it uses the TypeScript compiler API for TS/React (React Router,
Next.js, TanStack Router) and TS backends, a small go/types helper for
Go, and Python's ast for FastAPI/Flask/Django and friends. No LLM, no
network during analysis, and it never executes the analyzed repo's
code. It's deterministic and informs only; it never blocks a merge.
Limitations: only TS, Go and Python for now, and the reachability is
static, so dynamic dispatch and reflection make it fail closed
("unknown") rather than guess. I'd love feedback on the risk score
and on false positives in your codebase.
But the example PR... It said code generate with Claude, is it really still this bad? Last I was using SQLAlchemy with Claude was Opus 4.5, and it took some heavy coaching and skill gen not to generate code like this--but why even use SQLAlchemy at all if you're just going to use string interpolation jeez
https://github.com/erich-oliveira-winnin/full-stack-fastapi-...
Part of me thinks maybe because it's not popular or well documented, but SQLAlchemy has an eDSL of SQL, so you can skip the whole ORM/table mapping layer, and just use it to programmatically construct SQL syntaxAnecdata, datapoint of 1 personal experience: I had been using Opus 4.6 with no problems until yesterday, then it seemed like it was missing things constantly, introducing random bugs, forgetting basic stuff that was in its memory and skills and should never miss
Same thing happened on 2 separate sessions I had with Opus 4.6
Ended up switching to 5.5 and Fable, things improved instantly
This left me with a weird taste
Yes, this is called SQLAlchemy Core: http://docs.sqlalchemy.org/en/latest/core/
You can also just use psycopg (the Postgres driver layer) directly, it’s surprisingly ergonomic in my (limited) experience.
There was a post here recently, "Using LLM for confidence scoring is a bad idea", my mind went immediately to using LLMs for scoring risk. Seems like a bad idea. If there's anything we still need a human for, it's assessing risk.
However, the backend demo report seems longer and more complex than just reading the code. I’m trying to build something similar as well, and no one has quite nailed this yet.
The risk map / assisted review needs to be succinct and information dense, to guide your attention. My current philosophy is that we need to be annotating code, not replacing the entire code review interface with a bunch of text.
I thought mono meant one.
Trying to understand why the poster's own comment was flagged and killed but the original article remains up.
AI agents now write a lot of our code, and AI reviewers catch a lot of the line-level problems. But someone still has to press "merge", and that person has the same question every time: how risky is this, and where should I spend my attention? A 40-file PR where 38 files are isolated is very different from a 3-file PR that changes a model used by 23 endpoints.
farol is a CLI (and a GitHub Action) that answers that statically:
- classifies the PR (global / touches-existing / isolated / unknown) and gives a 0-100 merge risk score - ranks the files by risk and says why: what each one reaches (routes, endpoints, jobs), contract breaks in API responses, dependency changes, security patterns - diff coverage from your existing coverage report, so "38% of the changed lines are tested" sits next to the risky files - an HTML report where you mark files as reviewed and the score and test plan follow; incremental mode for "what changed since my last review"; suggested reviewers from git blame
How: it uses the TypeScript compiler API for TS/React (React Router, Next.js, TanStack Router) and TS backends, a small go/types helper for Go, and Python's ast for FastAPI/Flask/Django and friends. No LLM, no network during analysis, and it never executes the analyzed repo's code. It's deterministic and informs only; it never blocks a merge.
Live demo (two reports from a real PR, nothing to install): https://winnin.github.io/farol/demo/
Try it on a branch: npx @winninjs/farol --help
Limitations: only TS, Go and Python for now, and the reachability is static, so dynamic dispatch and reflection make it fail closed ("unknown") rather than guess. I'd love feedback on the risk score and on false positives in your codebase.