MurphyA/hacker-news huggingface dataset:
by mauvehaus | 2026-01-20 15:11:29+00:00
I have only ever owned cars with manual transmissions for my personal vehicles.

I would 100% get a vehicle without a manual for my next car if it’s an improvement over a manual. I’ve driven a handful of Priuses. I would definitely own one. I would definitely own an EV.

I have no desire to own an ICE-only vehicle with a CVT, automated manual, or conventional automatic. They add complexity and opaque failure modes. Last year I lost reverse in our plow truck (an automatic). Totally undiagnosable for me, nevermind fixable. Had a new used transmission put it, and it started bogging and lurching from a stop and up hills. Can’t work around it, can’t fix it. Sold the truck for $300 to someone who’s going to part it out (the engine wasn’t great either) and moved the plow onto a new used truck.

We’re not all stuck in the past. Some of us do understand the system well enough to be picky about believing something is an improvement.

Another example: CFL lightbulbs flat-out sucked. Avoided them as best as I could. Bought CREE LED bulbs at $20 apiece as soon as they came out at Home Depot.
by pjc50 | 2026-01-20 15:11:42+00:00
The corner is turning on this, with e.g. the Renault 5 EV being extremely popular: https://bestsellingcarsblog.com/2026/01/france-full-year-202...

I've test driven one, fun little car, decent provision of some non-touchscreen controls.

Ironically I think Tesla really opened up the EV market at all in the West by starting as a luxury option and working downwards. People don't want to feel they're taking a "hair shirt" option. "EVs are for rich people" has probably sold more cars than "EVs are for poor people" messaging would have.

The VW ID4 is winning the middle of the range family SUV market: https://electrek.co/2025/02/28/volkswagen-id-4-best-selling-...

It's not to my taste that bonnet lines have got higher, SUV style, but it appears to be what the public wants to buy.
by karmakurtisaani | 2026-01-20 15:11:44+00:00
But the problem is, students need to learn to do the easy things themselves before they can do the hard things with LLMs.

If you ask them to build a web browser when they can't do a hello world on their own, it's going to be a disaster. LLMs are like dumb juniors that you command, but students are less skilled than dumb juniors when they start programming classes..
by goku12 | 2026-01-20 15:11:59+00:00
Why is the news agency so surprised? Is it surprising that the civilians are a little on the edge after what ICE perpetrated in Minneapolis? A few mistaken identities are to be expected and it could happen to practically anyone. The good news is that the mob had enough sense to limit it to a verbal encounter, seriously consider their claims and leave the engineers physically unharmed. Has ICE ever listened to their victims when they protested that they were US citizens? How many innocent people have these mobs killed or seriously injured so far? The article gives you the impression that they are some sort of violent pillaging marauders. Is journalistic integrity a joke?

Meanwhile, the citizens need to calm down a little bit down. Anger and apprehension don't mix well.

(PS: I know who fox news is. It doesn't mean that I ever get used to their duplicity.)
by Tade0 | 2026-01-20 15:11:59+00:00
Similar experience here.

To me the limiting factor is, for lack of a better expression, the speed of taking responsibility for the agents' output.

I can't sign off a 1000 LoC change in 5 minutes, it's just not possible.

For this reason I don't believe people saying they've experienced a 20x speedup. No one who makes a living in this business is this much slower at writing than reading code that they don't hit a wall with the latter when the former is done by AI.
by elzbardico | 2026-01-20 15:12:11+00:00
Don't be so optimistic.

One of these days I was at a small grocery store in my hometown, and saw the screen of a POS system I had built in the late 90s with Visual C++ 6 running on Windows 10. The guy told me he got the system from the previous store owner he bought the shop from, it did most of he wanted it to do, and that his son is the guy who do things like upgrading the hardware, and writing small python programs that talk to the same SQLServer database to extend the system and implement some integration, so he sees no reason to get another system.
by dimanjet | 2026-01-20 15:12:19+00:00
Hey HN! I built this because I needed to sanitize user data before sending it to OpenAI/Claude APIs.

It detects 31 PII types (emails, phones, SSNs, credit cards, names, API keys, etc.) and can redact, mask, hash, or replace with fake data.

Self-hosted via Docker or pip install. Uses Microsoft Presidio + spaCy NER under the hood.

Happy to answer any questions!
by 9rx | 2026-01-20 15:12:57+00:00
You're right, but learning can take place when you need it. There is no real advantage to learning something ahead of time. The bottleneck is having awareness of what is out there to learn. You can't learn about what you don't know exists. Looking at calculus solutions all day should give a sense of what calculus can be used for, so that it is in your back pocket when the time you need it comes.

Well, at least it used to be the bottleneck. Nowadays you can just ask an LLM. For all their faults, they are really good at letting you know about what tools exist in out there in the world, surfacing more than you could ever come to know about even if all you did was read about what exists all day, every day.
by jorgeleo | 2026-01-20 15:12:57+00:00
I did the same experiment as you, and this is what I learned:

https://www.linkedin.com/pulse/concrete-vibe-coding-jorge-va...

The bottom line is this:

* The developer stop been a developer, and becomes a product designer with high technical skills.

* This is a different set of skills than than a developer or a product owner currently have. It is a mix of both, and the expectations of how agentic development works need to be adjusted.

* Agents will behave like junior developers, they can type very fast, and produce something that has a high probability to work. They priority will be to make it work, not maintainability, scalability, etc. Agents can achieve that if you detail how to produce it.

* The working with an agent feels more like mentoring the AI than ask and receive.

* When I start to work on a product that will be vibe coded, I need to have clear in my head all the user stories, code architecture, the whole system, then I can start to tell the agent what to build, and correct and annotate in the md files the code quality decisions so it remembers them.

* Use TDD, ask the agent to create the tests, and then code to the test. Don't correct the bugs, make the agent correct them and explain why that is a bug, specially with code design decisions. Store those in AGENTS.md file at the root of the project.

There are more things that can be done to guide the agent, but I need to have clear in an articulable way the direction of the coding. On the other side, I don't worry about implementation details like how to use libraries and APIs that I am not familiar with, the agent just writes and I test.

Currently I am working on a product and I can tell you, working no more than 10 hours a week (2 hours here, 3 there, leave the agent working while I am having dinner with family) I am progressing at I would say 5 to 10 times faster than without it. So, yeah it works, but I had to adjust how I do my job.
by lukebechtel | 2026-01-20 15:12:58+00:00
1. Start with a plan. Get AI to help you make it, and edit.

2. Part of the plan should be automated tests. AI can make these for you too, but you should spot check for reasonable behavior.

3. Use Claude 4.5 Opus

4. Use Git, get the AI to check in its work in meaningful chunks, on its own git branch.

5. Ask the AI to keep am append-only developer log as a markdown file, and to update it whenever its state significantly changes, or it makes a large discovery, or it is "surprised" by anything.
by corv | 2026-01-20 15:13:06+00:00
You're right that blocking on every operation would defeat the purpose! Shannot is able to auto-approve safe operations for this reason (e.g. read-only, immutable)

So the agent can freely explore, check logs, list files, inspect service status. It only blocks when it wants to change something (install a package, write a config, restart a service).

Also worth noting: Shannot operates on entire scripts, not individual commands. The agent writes a complete program, the sandbox captures everything it wants to do during a dry run, then you review the whole batch at once. Claude Code's built-in controls interrupt at each command whereas Shannot interrupts once per script with a full picture of intent.

That said, you're pointing at a real limitation: if the fix genuinely requires a write to test a hypothesis, you're back to blocking. The agent can't speculatively install a package, observe it didn't help, and roll back autonomously.

For that use case, the OP's VM approach is probably better. Shannot is more suited to cases where you want changes applied to the real system but reviewed first.

Definitely food for thought though. A combined approach might be the right answer. VM/scratch space where the agent can freely test hypotheses, then human-in-the-loop to apply those conclusions to production systems.
by brianpbeau | 2026-01-20 15:13:37+00:00
Even before that we had "sweeps week" in broadcasting. This was the period of time where the big networks pushed out their biggest shows, miniseries and end of season episodes. This would lead into the nightly news which would also put together an extended expose on a headline-grabbing story. These stories would be heavily advertised during the prime time shows and would often be told over a period of nights. All of this was in service for high ratings that could be used to increase advertising rates.

So yes, all of this has happened before and will continue to happen as long as we live in a capitalist society.
by vessenes | 2026-01-20 15:14:01+00:00
Yep, it works. Like anything getting the most out of these tools is its own (human) skill.

With that in mind, a couple of comments - think of the coding agents as personalities with blind spots. A code review by all of them and a synthesis step is a good idea. In fact currently popular is the “rule of 5” which suggests you need the LLM to review five times, and to vary the level of review, e.g. bugs, architecture, structure, etc. Anecdotally, I find this is extremely effective.

Right now, Claude is in my opinion the best coding agent out there. With Claude code, the best harnesses are starting to automate the review / PR process a bit, but the hand holding around bugs is real.

I also really like Yegge’s beads for LLMs keeping state and track of what they’re doing — upshot, I suggest you install beads, load Claude, run ‘!bd prime’ and say “Give me a full, thorough code review for all sorts of bugs, architecture, incorrect tests, specification, usability, code bugs, plus anything else you see, and write out beads based on your findings.” Then you could have Claude (or codex) work through them. But you’ll probably find a fresh eye will save time, e.g. give Claude a try for a day.

Your ‘duplicated code’ complaint is likely an artifact of how codex interacts with your codebase - codex in particular likes to load smaller chunks of code in to do work, and sometimes it can get too little context. You can always just cat the relevant files right into the context, which can be helpful.

Finally, iOS is a tough target — I’d expect a few more bumps. The vast bulk of iOS apps are not up on GitHub, so there’s less facility in the coding models.

And any front end work doesn’t really have good native visual harnesses set up, (although Claude has the Claude chrome extension for web UIs). So there’s going to be more back and forth.

Anyway - if you’re a career engineer, I’d tell you - learn this stuff. It’s going to be how you work in very short order. If you’re a hobbyist, have a good time and do whatever you want.
by jackcarter | 2026-01-20 15:14:15+00:00
"At some point I realized that rather than do something else until it finishes, I would constantly check on it to see if it was asking for yet another permission, which felt like it was missing the point of having an agent do stuff"

Why don't Claude Code & other AI agents offer an option to make a sound or trigger a system notification whenever they prompt for approval? I've looked into setting this up, and it seems like I'd have to wire up a script that scrapes terminal output for an approval request. Codex has had a feature request open for a while: https://github.com/openai/codex/issues/3052
by rurban | 2026-01-20 15:14:28+00:00
Distort the beautiful scheme language via a limited python evaluator? Oh my.
I.e. in scheme a math op is defines as applying the ops to all its args.

(+ 0 1 2 3 4 5) => (apply '+ '(0 1 2 3 4 5))

In this python code the op becomes a string (from a symbol), and a binop only. It can only do ("+" 0 1), but not more.
That's why a proper book teaches this meta evaluator starting with apply, not such limited lambda's.
by p_l | 2026-01-20 15:14:42+00:00
In my experience[1], the fsck for given filesystem will happily replicate the errors, sometimes in random ways, because often it cannot figure which road to take in face of inconsistency. If anything, OpenZFS built upon that by now documenting the previously deeply hidden option to "rewind" ZFS uberblock if the breakage is recent enough.

[1] I've seen combination of ubuntu bug in packaging (of grub, of all things) and e2fsck nearly wipe a small company from existence, because e2fsck ended up trusting the data it got from superblock when it was not consistent.
by bluGill | 2026-01-20 15:14:45+00:00
That makes it even more realistic. I have the charger in my garage, I happened to need a charger to get home on my last trip (120 mile round trip, the car claimed 220 miles of charge but that didn't account for the cold winter), but I had to open an app and such just to use it. (at least I had the app and an account - but my credit card was expired so I had to type numbers to get it activated). I had to search for that charger - there was exactly one charger within 30 miles (only 7kw, but it gave me enough range to get home while I ate lunch).

Meanwhile I passed half a dozen gas stations. No app/account needed at any of them, just tap/swipe my credit card and fill.

Most people don't have the charging problem often, but when you make a mistake you sometimes will need it. The system doesn't work. There needs to be chargers all over, and they need to be quick/easy. I don't want to download an app for a charger I will likely never visit again in my life.
by gnarlouse | 2026-01-20 15:15:22+00:00
A knock on consequence if everybody did this:

- HR is no longer inundated with garbage resumes

- Hiring managers can actually focus on the resumes in their inbox and assume that people are genuinely interested in the role.

- The whole system works more efficiently.

From day one I thought the whole notion of AI in the hiring process (on both sides: candidates submitting AI resumes, HR filtering resumes with AI) was positively absurd. I hope more people catch on.
by rincebrain | 2026-01-20 15:15:34+00:00
The two obvious examples that come to mind are native encryption bugs and spacemap issues.

Nothing about walking the entire tree of blocks and checking hashes validates the spacemaps - they only come up when you're dealing with allocating new blocks, and there have been a number of bugs where ZFS panics because the spacemaps say something insane, so you wind up needing to readonly import or discard the ZIL because it panics about trying to allocate an already-allocated segment if you import RW - and if your ondisk spacemaps are inconsistent in a way that discarding the ZIL doesn't work around, you would need some additional tool to try and repair this, because ZFS has no knobs for it.

Native encryption issues wouldn't be noticed because scrubbing doesn't attempt to untransform data blocks - you indirectly do that when you're walking the structures involved, but the L0 data blocks don't get decompressed or decrypted, since all your hashes are of the transformed blocks. And if you have a block where the hash in the metadata is correct but it doesn't decrypt, for any reason, scrub won't notice, but you sure will if you ever try to decrypt it.
by bobjordan | 2026-01-20 15:16:03+00:00
Great question, I let Claude help answer this...see below:

The key differences are:

1. Static vs Runtime Analysis
Linters use AST parsing to analyze code structure without executing it. Tests verify actual runtime behavior. Example from our datetime_linter:

tree = ast.parse(file_path.read_text())
for node in ast.walk(tree):
if isinstance(node, ast.Import):
if alias.name == "datetime":
# Violation: should use pendulum

This catches import datetime syntactically. A test would need to actually execute code and observe wrong datetime behavior.

2. Feedback Loop Speed
- Linters: Run in pre-commit hooks. Agent writes code → instant feedback → fix → iterate in seconds
- Tests: Run in CI. Commit → push → wait minutes/hours → fix in next session

For AI agents, this is critical. A linter that blocks commit keeps them on track immediately rather than discovering violations after a test run.

3. Structural Violations
For example, our `fastapi_security_linter` catches things like "route missing TenantRouter decorator". These are structural violations - "you forgot to add X" - not "X doesn't work correctly." Tests verify the behavior of X when it exists.

4. Coverage Exhaustiveness
Linters scan all code paths structurally. Tests only cover scenarios you explicitly write. Our org_scope_linter catches every unscoped platform query across the entire codebase in one pass. Testing that would require writing a test for each query.

5. The Hybrid Value
We actually have both. The linter catches "you forgot the security decorator" instantly. The test (test_fastapi_authorization.py) verifies "the security decorator actually blocks unauthorized users at runtime." Different failure modes, complementary protections.

Think of it like: linters are compile-time checks, tests are runtime checks. TypeScript catches string + number at compile time; you don't write a test for that.
by vladvasiliu | 2026-01-20 15:16:18+00:00
> People aren't being asked to dump their current reliable vehicles

Depends on where the people live. In France, that's just about what they're asked. If their car is "too old" (reliability doesn't matter, only age), they may no longer drive in Paris and some other major cities on weekdays from 6 AM to 8 PM or something like that.

https://www.france.fr/en/article/crit-air-anti-pollution-veh...
by piperswe | 2026-01-20 15:16:26+00:00
I own a Panamera and my husband owns an M850i. The Panamera drives like a Porsche, while the M850i is comparatively a boat. I'm not a big fan of how much suspension travel the M850i has; the Panamera has exactly the right amount to feel sporty but still comfortable, just like our 718 has.
by traceroute66 | 2026-01-20 15:16:27+00:00
I am in the same boat as you.

The only positive antigenic coding experience I had was using it as a "translator" from some old unmaintained shell + C code to Go.

I gave it the old code, told it to translate to Go. I pre-installed a compiled C binary and told it to validate its work using interop tests.

It took about four hours of what the vibecoding lovers call "prompt engineering" but at the end I have to admit it did give me a pretty decent "translation".

However for everything else I have tried (and yes, vibecoders, "tried" means very tightly defined tasks) all I have ever got is over-engineered vibecoding slop.

The worst part of of it is that because the typical cut-off window is anywhere between 6–18 months prior, you get slop that is full of deprecated code because there is almost always a newer/more efficient way to do things. Even in languages like Go. The difference between an AI-slop answer for Go 1.20 and a human coded Go 1.24/1.25 one can be substantial.
by 3vidence | 2026-01-20 15:16:51+00:00
Googler opinions are my own.

If agentic coding worked as well as people claimed on large codebases I would be seeing a massive shift at my Job... Im really not seeing it.

We have access to pretty much all the latest and greatest internally at no cost and it still seems the majority of code is still written and reviewed by people.

AI assisted coding has been a huge help to everyone but straight up agentic coding seems like it does not scale to these very large codebases. You need to keep it on the rails ALL THE TIME.
by saurabhjain1592 | 2026-01-20 15:17:43+00:00
Good question.

By deterministic policy enforcement we mean rule-based checks that evaluate to an explicit allow or block decision at execution time. Today that includes a mix of regex-based checks (for example PII patterns), structured detectors, and hard limits or business rules like cost caps, rate limits, and permission constraints. These policies are evaluated inline before model or tool calls, so the outcome is predictable and auditable rather than probabilistic.

On cost tracking: yes, AxonFlow captures per-call metadata including model, tokens, provider, and cost, and attributes it to user, workflow, and tenant. In gateway mode this is per-call audit logging, and in proxy mode it extends across multi-step workflows so you can see cost accumulation per user or execution. We also recently shipped Workflow Control Plane which tracks policy evaluation and cost accumulation across multi-step agent executions, so you get a single audit trail and cost rollup for an entire workflow, not just individual calls. That's been a common pain point we've seen with teams running agents in production.
by antonvs | 2026-01-20 15:17:58+00:00
> Anyone who claims AI is great is not building a large or complex enough app

That might be true for agentic coding (caveat below), but AI in the hands of expert users can be very useful - "great" - in building large and complex apps. It's just that it has to be guided and reviewed by the human expert.

As for agentic coding, it may depend on the app. For example, Steve Yegge's "beads" system is over a quarter million lines of allegedly vibe-coded Go code. But developing a CLI like that may be a sweet spot for LLMs, it doesn't have all the messiness of typical business system requirements.
by Fiveplus | 2026-01-20 15:17:59+00:00
The collapse in IPv4 transfer prices is what caught my eye here, dropping from a ~$55 peak in 2021 to a mean of $22 in early 2026 (figure 12).

This validates my hypothesis that the run-up in 2020–2022 was an artificial scarcity bubble driven largely by hyperscalers. AWS was right up there stockpiling before they shifted their pricing model. Once AWS introduced the hourly charge for public IPv4 addresses (effectively passing the scarcity cost to the consumer), their acquisition pressure vanished. The text notes Amazon stopped announcing almost 15M addresses in Nov 2025. I think they have moved from aggressive accumulation to inventory management.

We are seeing asset stranding in real-time. The market has realized that between the AWS tax and the efficacy of mobile CGNAT, the desperate thirst for public v4 space was not infinite. I'm curious to hear more takes on this.
by baal80spam | 2026-01-20 15:18:01+00:00
> Use Claude 4.5 Opus

In my org we are experimenting with agentic flows, and we've noticed that model choice matters especially for autonomy.

GPT-5.2 performed much better for long-running tasks. It stayed focused, followed instructions, and completed work more reliably.

Opus 4.5 tended to stop earlier and take shortcuts to hand control back sooner.