MurphyA/hacker-news huggingface dataset:
by ehnto | 2026-02-24 05:19:41+00:00
That's a great insight about iterating on bespoke tools. I have seen the most speed up when diving into new tools, or making new tools as AI can make the initial jump quite painless, and I can get straight to the problem solving. But I get barely any speedup using it on legacy projects in tools I know well. Often enough it slows me down so net benefit is nil or worse.

Another commentor said it makes the easy part easy, and the hard part harder, which I resonate with at the moment.

I am pretty excited by being able to jump deep into real problems without code being the biggest bottleneck. I love coding but I love solving problems more, and coding for fun is very different to coding for outcomes.
by PhiniteAI | 2026-02-24 05:19:48+00:00
Hey HN,

We are Phinite. We build infrastructure for deploying production-ready multi-agent systems.

The problem: building multi-agent systems requires you to think
in graphs — nodes, edges, agent roles, tool connections, prompts.
Most engineers spend more time architecting the workflow than
actually solving the business problem.

So we built Phinite Aura — you describe what you want in plain
English, and it designs the full multi-agent workflow for you:
agents, prompts, tool connections, and the underlying code.

Example from our own testing:
"I want to build an e-commerce product comparison assistant"

Aura generated: an orchestrator agent, 3 parallel product data
extractor agents, a comparative analysis agent, connected tools
(scrapers, APIs), and all the prompts. In under 60 seconds.

You can then modify it conversationally — "add a third product" —
and it updates the entire workflow graph including agent logic,
variables, and connections.

The canvas is live, versioned, and deployable. Integrations
include Salesforce, Slack, GitHub, Google Sheets, Notion, and
23 others out of the box. Built-in dev/UAT/prod environments
with Kubernetes-isolated traffic.

We built this because we were frustrated watching engineering
teams spend weeks on workflow architecture before writing a
single line of business logic.

Happy to discuss the architecture, how Aura handles ambiguous
instructions, or where it still fails.

Try it: www.phinite.ai
by singhsanjay12 | 2026-02-24 05:21:47+00:00
The idea is attractive (especially for draining), but once you try to map arbitrary inbound client connections onto backend-initiated "reverse" pipes, you end up needing standardized semantics for multiplexing, backpressure, failure recovery, identity propagation, and streaming! So, you're no longer just standardizing "reverse HTTP", you’re standardizing a full proxy transport + control plane. In practice, the ecosystem standardized draining/health via readiness + LB control-plane APIs and (for HTTP/2/3) graceful shutdown signals, which solves the draining problem without flipping the fundamental accept/connect roles.
by bagacrap | 2026-02-24 05:22:11+00:00
I've tried very earnestly to use opus 4.5 to get rid of some backlog tasks that were too tedious to do manually. It turns out that they're still extremely tedious because I have to make every single non trivial decision for the model, unless I don't care one iota about the long term sustainability of the code base. And by long term, I mean more than a week. They're good for saving keystrokes or doing fuzzy searches for me. "Design"? No, that is an anthropomorphism.
by runarberg | 2026-02-24 05:22:31+00:00
I‘m not (yet) a citizen of the USA, but I’ve lived there for a while. As I understand it, there is hardly any political opposition in this country. I would actually describe it as a controlled opposition. A lot of people here tend to think the only role of the opposition is to run the right candidate and win the next election. As such, there is no real resistance when the majority government oversteps their boundaries.

To make matters worse, labor unions are equally politically inactive, and most often their only political moves are endorsing candidates. When they do voice support for or opposition against bills, those bills are often stuff related to their industry, and seldom do they actually oppose an over reaching government by threatening general strikes etc.

The press here is also very right leaning. All the big media are owned by capitalist conglomerates and as such most people never hear real challenges to the capitalist power structure. As long as the government class acts favorable to the capitalist interest, then the press has aligning interest, and is thus heavily incentivized to never challenge the government to much.
by anonzzzies | 2026-02-24 05:22:48+00:00
We run production on servers 15 years old for our company/clients. Servers now are far faster and the software barely changes performance characteristic wise. We run Java, Perl & PHP ERP/departmental type projects; nothing that gets added makes anything slower and I don't see that happening either. Unless clients will want vastly different things, which, you know, they won't as they are big sluggish companies.
by tl2do | 2026-02-24 05:23:18+00:00
I live in Japan, and our repair framework feels weak.

A lot of it is based on industry association rules (業界団体ルール), not enforceable regulation. For example, major electronics companies sometimes disclose a parts retention period (部品保有期限), like keeping parts for X years, but that is mostly traditional large companies.

On repair policy/enforcement, the EU and US seem more advanced than Japan. That is why stories like this (farmers pushing back on dealer lock-in and repair access) are interesting to me.
by umairnadeem123 | 2026-02-24 05:23:32+00:00
the spec-first approach is the real insight here. in my experience with AI-assisted codegen, having the model write a detailed spec doc before any implementation code cuts the iteration cycles dramatically. without it the agent just thrashes between plausible-looking approaches. with a good spec it can stay coherent across thousands of lines because it has something to ground against.

the two-month timeline is interesting too - would love to know the actual compute cost. for a working kernel driver that would have otherwise never existed, even $500 in API calls seems like a bargain.
by SilentM68 | 2026-02-24 05:24:13+00:00
Yup, I am a fan and watched that concept video when it came out.
I'm hoping he makes some sort of return, perhaps involving the
Star Trek Picard Season 3 reveal that Kirk's body is being kept
on ice at Section 31, on Daystrom Station. I would imagine that
there are many ways that Kirk could be brought back, perhaps
Kirk's body could have ended up being stored in a transporter's
buffer similar to how the episode "Relics" from Star Trek: The
Next Generation, Captain Montgomery Scott (Scotty) is discovered
alive in a transporter buffer after being trapped for 75 years
on a Dyson sphere. The transporter has been rigged to sustain
two life signals, allowing Scott to survive by maintaining a
diagnostic cycle. His pattern remains intact, allowing him to
be rematerialized after being rescued by the USS Enterprise.
Another possibility is being back due to meddling from the Q
or an evolved V'Ger. There's been published a comic where some
of the old crew from multiple TV projects have been brought
back to help solve the killings of some of Star Trek's Gods
by an unknown figure. Emperor Kahless is eventually revealed
to be Star Trek’s god-killer. Among the many cameos in the
series are Benjamin Sisko, Jake Sisko, Kira Nerys, Odo, Worf,
Alexander Rozhenko, Spock, Scotty, Uhura, B'Elanna Torres,
Tom Paris, Ro Laren, and others like Q, Kahless (clone/Emperor),
and crossovers with Picard-era or TOS characters. It does not
have to be a TV series but could be a limited TV mini-series.
The Star Trek financial gods wasted big money on a badly done
Section 31 movie. I don't see why they don't do something more
similar to Star Trek comic and Television canon.
by socalgal2 | 2026-02-24 05:24:17+00:00
Did AI write the post?

First section says "The models that passed the car wash test: ...Gemini 2.0 Flash Lite..."

A section or 2 down it says: "Single-Run Results by Model Family: Gemini 3 models nailed it, all 2.x failed"

In the section below that about 10 runs it says: 10/10 — The Only Reliable AI Models ... Gemini 2.0 Flash Lite ..."

So which it is? Gemini 2.x failed (2nd section) or it succeeded (1st and 3rd) section. Or am I mis-understanding
by layla5alive | 2026-02-24 05:24:24+00:00
Yes, in 2D, but NAND has been using layers for a while. We call HBM interposers 2.5D. 3D breakthrough would be pretty easy but for those pesky problems like power delivery and cooling. (/s)

But give that time (e.g. microfluidics) - something interesting is that it would be extra hard to use all layers at once, but NN might be a good fit, imagining that computation will be sparse (subsets activating simultaneously)...
by umairnadeem123 | 2026-02-24 05:24:35+00:00
the practical value here is for regulated domains. in healthcare and finance you often cant deploy a model at all unless you can explain why it made a specific decision. token-level attribution that traces back to training data sources could satisfy audit requirements that currently block LLM adoption entirely.

curious how the performance compares to a standard llama 8b on benchmarks - interpretability usually comes with a quality tax.
Show HN: Build Your Own CLI Coding Agent in Python
by primaprashant | 2026-02-24 05:24:54+00:00
Ran a hands-on workshop in Tokyo where ~50 engineers built a CLI coding agent from scratch in Python and now, I've converted it into a self-paced exercise.

GitHub Repo: https://github.com/primaprashant/alduin

This should help in getting a better understanding of what goes on inside the coding agents. You will iteratively implement the core agent loop present in all coding agents like Claude Code and Codex starting from a skeleton repo with a basic input loop and no LLM. Over 7 phases, you will add the Anthropic API and implement tools (read file, edit file, bash). Each phase has hints and a reference implementation if you get stuck.

Should take ~3-5 hours and in the end, you'll have your own coding agent which you can use on other project and codebases as well.

Feedback and PRs welcome. Happy to answer any questions.
by umairnadeem123 | 2026-02-24 05:25:24+00:00
the most interesting finding here is the variance across runs. GPT-5 going 7/10 means its internal representation is unstable for this kind of reasoning - it sometimes activates the right world-model pathway and sometimes doesnt. thats actually more concerning than a model that consistently fails, because you cant predict when itll get things right.

forced choice without a "need more context" option is also doing a lot of work here. in production systems i always give models an explicit escape hatch to say they need clarification. cuts wrong-answer rates roughly in half in my experience.
by umairnadeem123 | 2026-02-24 05:26:12+00:00
the real shift is that throwaway code became viable for production workflows. i used to spend days writing reusable utility libraries. now i generate single-purpose scripts, run them once, and delete them. the economics of code reuse have fundamentally changed when generation is cheaper than comprehension.

the downstream bottleneck is real though. built a video production pipeline recently - generating the python glue code took maybe 10% of total project time. the other 90% was testing edge cases, tuning ffmpeg parameters, and figuring out why API responses were subtly different between providers. cheap code just means you hit the hard problems faster.
by Dylan16807 | 2026-02-24 05:26:13+00:00
So where did this 7% number come from?

Another way to look at that is one parent brings in 100% of their income, and when the second parent wants to get a job they have to sacrifice 14% of their pay to get childcare while they're away. That seems low to me.

Another way to analyze this is to look at how many children a single worker has to take care of to hit the same pay. If childcare for 2 kids is 14% of a salary, and salary is half the cost of running a daycare, then you need 29 kids per childcare employee?? How is that supposed to work?

Edit: And yes I'm aware subsidies can exist but this hits an area where the subsidies are so high that the 7% number still needs explanation.
by umairnadeem123 | 2026-02-24 05:26:59+00:00
the connection draining feature is really clever. weve dealt with connection storms from app crashes before and the standard playbook is just to restart everything and hope the thundering herd doesnt immediately repeat. having the proxy absorb that chaos at the wire protocol level is much better than trying to handle it in application code, especially when you have 20+ microservices all competing for the same connection pool.

the unique_id() sequence is interesting too - monotonically increasing cross-shard IDs solve a real pain point for pagination. with UUIDs you end up doing cursor-based pagination with composite keys which makes your ORM code ugly fast.
by ehnto | 2026-02-24 05:27:14+00:00
My intuition from talking to people across different parts of the industry, is that adoption at bigger companies is really limited or slow, or totally banned. Additionally some developers are not seeing it help their specific roles all that much anyway. This is hard to level with success other people are having, but software is a super broad discipline which I think explains a lot of the mixed success stories.

It seems to depend a lot on the industry and niche you're in, working at an agency I get experience across many different projects and industries and sometimes you are just at the edge of AIs training and it can get very unhelpful. Noting many if not most companies are working on proprietary code in donain specific problems, that isn't all that surprising either.
by thaumasiotes | 2026-02-24 05:27:26+00:00
Well, from what I've read...

Spiders have 8 eyes. As with vertebrates, this number doesn't change, but there is variation in what it means.

A "normal" spider doesn't really use its eyes. It just has them.

Some spiders are different and rely on their vision. Those spiders have two primary eyes, which they rely on, and six secondary ones, which they don't.

Moving to insects, they often have compound eyes. Two compound eyes. A mantis has two primary compound eyes and three secondary non-compound eyes.

All this convergence suggests to me that even if you have the option to grow more eyes, the correct number is two.
by umairnadeem123 | 2026-02-24 05:27:46+00:00
the real value proposition here is correctness guarantees that LLMs fundamentally cant provide. when an LLM says 2+2=4 it arrived there statistically, not computationally. for anything safety-critical - engineering tolerances, drug dosage calculations, financial modeling - you want a deterministic engine producing the answer and the LLM just translating between human intent and formal queries.

the CAG framing is clever marketing but the underlying idea is sound: treat the LLM as a natural language interface to a computational kernel rather than the computation itself. weve been doing something similar with python subprocess calls from agent pipelines and it works well. the question is whether wolfram language offers enough over python+scipy+sympy to justify the licensing cost and ecosystem lock-in.
by locknitpicker | 2026-02-24 05:27:47+00:00
> LLMs make the typing cheap, but they don’t make the reasoning cheap.

LLMs lower the cost of copy/pasting code around, or troubleshooting issues using standard error messages.

Instead of going through Stack Overflow to find how to use a framework to do some specific thing, you prompt a model. You don't even need to know a thing about the language you are using to leverage a feedback loop.

LLMs lower the cost of a multitude of drudge work in developing software, such as having to read the docs to learn how a framework should be used to achieve a goal. You still need to know what you are doing, but you don't need to reinvent the wheel.
by stephen_g | 2026-02-24 05:27:59+00:00
Yeah, main issue with Swift is that the c++ interop (which was absolutely bleeding-edge) still isn't to the point of being able to pull in parts of the Ladybird codebase.

If I recall correctly, part of this was around classes they had that replaced parts of the STL, whereas the Swift C++ interop makes assumptions about things with certain standard names.
by hattmall | 2026-02-24 05:28:25+00:00
But it is a wildly variated, almost meaningless diagnosis. 3 of my 4 grandparents got Alzheimer's diagnosis as well as my mom and mother-in-law. The variation of progression and symptoms is so wide that it really seems like a catch-all. One grandmother was fine until about 72 and in 2 years forgot who people were and 4 years had lost all executive function and passed away. The other one was diagnosed in her early 80s and lived to be 96 with no major progression, like slightly more repeating, but never forgetting people or not knowing how to talk etc. Similar dichotomy between my mother and mother-in-law but with considerably different presentations of symptoms.

It's a weird disease and IMO not even really a disease it's a bunch of different causes of cognitive impairment under one umbrella but shouldn't be separated out much further to find actual causes and treatments.
by umairnadeem123 | 2026-02-24 05:28:32+00:00
the most underappreciated aspect of AI-assisted language migration is that it changes the cost-benefit analysis of which language to target. previously choosing rust for a browser engine meant accepting 3-5x slower development velocity vs c++. if AI closes that gap to near parity, the calculus shifts dramatically - you get rusts safety guarantees essentially for free in terms of developer time.

the two-week timeline for 25k lines is striking. even accounting for the human review overhead, thats probably 4-6x faster than manual porting. and the byte-for-byte verification approach means the speed doesnt come at the cost of correctness. this could be a template for how other large c++ codebases approach incremental rust adoption.
by umairnadeem123 | 2026-02-24 05:29:19+00:00
the opinionated workflow is the real differentiator here. wireshark gives you everything and lets you drown in it. the "weird stuff" detector that surfaces retransmits and resets first is exactly the right call - in my experience debugging production issues, 80% of the time you just need to find the thing thats broken, not understand every packet.

would be great to see a "save investigation" feature that exports a summary of what you found. half the value of packet analysis in production is being able to hand someone a concise report saying "here is what happened" without making them also learn wireshark.
by handoflixue | 2026-02-24 05:29:20+00:00
Oh wow, Sonnet still isn't handling it well:

Opus 4.6: Drive (https://claude.ai/share/d57fef01-df32-41f2-b1dc-07de7916bdc7)

Opus 4.5: Drive (https://claude.ai/chat/a590cac1-100a-490b-b0a2-df6676e1ae99)

Opus 3.0: Walk (https://claude.ai/chat/372c144c-d6eb-43f5-b7ea-fd4c51c681db)

Sonnet 4.6: Walk (https://claude.ai/share/1f2a80f3-4741-40a5-8a05-7349ea1a17e5)

Sonnet 4.5: Walk (https://claude.ai/share/905afeb6-ffc9-4b4b-a9ee-4481e5cfd527)

Favorite answer, using my default custom instructions: "Drive. Walking there means... leaving your car at home? Walk it there on a leash? Walk if you want the exercise, but you're bringing the car either way."
by stevenjgarner | 2026-02-24 05:29:32+00:00
In 2017, Apple and John Deere famously joined forces in Nebraska to fight an early R2R bill. An Apple lobbyist told Nebraska legislators that passing the bill would make the state a "Mecca for hackers," a talking point that has since been used by various industries to argue that opening up hardware leads to security risks. [1] There is a very real need for manufacturers to lobby through shared organizations because they recognize that a Right to Repair victory for one product is legally and logically a victory for all products.

The problem is that both sides are correct. The core of the R2R argument is about ownership instead of merely "licensing" from the manufacturer. Repair monopolies create an artificial scarcity, destroying economic efficiency, market competition and planned obsolescence (defeating environmental stewardship). A centralized repair model is a single point of failure, weakening resilience and national security.

Manufacturers have a strong argument against right-to-repair from the perspective of system integrity and safety - one can imagine unintended consequences and liability cascades from imperfect repair. Protection of intellectual property isn't just about software piracy and trade secrets, as opening up firmware access creates a cybersecurity nightmare of backdoors, raising environmental and regulatory compliance issues. The authorized dealer model isn't just about a monopoly - it’s about a guaranteed standard of care.

The current compromise is a subscription-based access model Memorandum of Understanding, where for a tiered subscription the John Deere customer gets a restricted version of the dealer's software [2]. The "Gotcha" in the MOU is that many farmers feel this was a bad trade because the manufacturer can change the price or the terms of the website at any time — whereas a law would be permanent.

[1] https://www.techdirt.com/2018/02/01/apple-verizon-continue-t...

[2] https://www.deere.com/en/our-company/repair/customer-service...
by josephg | 2026-02-24 05:29:34+00:00
> I also think there are plenty of things we are unlikely to get to even in an edition, and that might never happen without a completely different language.

Yes, this is my feeling too. All programming languages - perhaps, all computer programs - must decide how malleable they should be. Fast moving systems are exciting, but they're very annoying to use, or build on top of. I think generally we want a very slow moving language for infrastructure software, like databases or the linux kernel. Slow moving languages are often good for users, because they don't need to learn new things or rewrite existing software. (I think thats one of the reasons python3 was so poorly received.)

It might be too late for large changes in rust. This a sign of maturity, but its also a little bit sad. I want all those features you mention too.

Some day. Maybe LLMs will help somehow. Rust is, thankfully, not the last programming language humans will invent.
by CobrastanJorji | 2026-02-24 05:29:54+00:00
I don't think 30% of people can't reason. I think 30% of people will fail fairly simple trick questions on any given attempt. That's not at all the same thing.

Some people love riddles and will really concentrate on them and chew them over. Some people are quickly burning through questions and just won't bother thinking it through. "Gotta go to a place, but it's 50 feet away? Walk. Next question, please." Those same people, if they encountered this problem in real life, or if you told them the correct answer was worth a million bucks, would almost certainly get the answer right.
by umairnadeem123 | 2026-02-24 05:30:05+00:00
the practical implication thats easy to miss: this isnt just about replacing coreutils with rust equivalents. its about building a dependency chain where distro-level packages can start depending on rust libraries natively. once canonical maintains rust toolchains as first-class build infrastructure, third-party package maintainers can start writing new system utilities in rust without the friction of bootstrapping a separate toolchain.

the dd incompatibility with makeself/cuda installers mentioned in this thread is exactly the kind of edge case that matters at distro scale. gnu coreutils has 40+ years of quirk accumulation and lots of real-world scripts depend on undocumented behavior. the rust rewrites need to be bug-compatible, not just spec-compatible.