MurphyA/hacker-news huggingface dataset:
by vladdoster | 2026-03-11 06:22:18+00:00
Location: Nashville, TN
Remote: true
Willing to relocate: boston || nashville
Technologies: Python, Django, IaC, Terraform, Golang, CI/CD, Azure/AWS, {ba,z}sh, Lua, Springboot,
Redis, Docker, Open Service Broker, {,non}relational & vector dbs, shared services, SOA
Email: mvdoster@gmail.com

resume: https://linkedin.vdoster.com && http://github.com/vladdoster

sr. cloud software engineer @ Mastercard (5 years)

Designed and built Platform-as-a-Service for microservice workloads running on Azure and AWS
to migrate on-prem workloads to the cloud. The platform operates globally, allowing teams to
deploy to several Azure-backed regions worldwide. Helped plan and create regional landing zones
in Azure for application teams wanting to perform globally. Designed platform components using
Terraform, Go, and Node; developed support tooling using Bash, Python, and Azure CLI. De-risk the
platform by addressing security vulnerabilities and ensuring the platform is PCI compliant.

api/devops software engineer @ Harvard Medical School (2 years)

Worked as part of the DevOps team to manage a 15k core CentOS HPC cluster. Designed and implemented
Python-based security scanning pipeline to securely deploy & run Apptainer images via SLURM.
Coordinated cross-team emergency patching eﬀorts. Collaborated with researchers and engineers across
3-letter institutions to deliver secure research infrastructure. Built CI/CD Pipelines with GitHub, Jenkins,
to automate code builds, testing, and deployments. Wrote Puppet modules to manage configuration of HPC fleet.
Wrote custom Puppet module & Python scripts to automatically remediate Tenable findings.

full-stack software engineer @ Global Prior Art (3 years)

Used Django, Celery, Selenium grid, and AWS API Gateway for IP rotation to scrape USPTO patent PDFs,
apply CV for data extraction, and generate reports with relevant claims, saving employees 4-5 hours daily.
Wrote E2E black-box/fuzzing unit tests. Automated on-premise deployment via Github Actions and Ansible.

maintainer @ Zinit (5 years)

Develop and maintain a zsh plugin manager on Github with 4k stars. Implemented unit tests,
containerization, bug fixes, and new features.
See: https://github.com/zdharma-continuum/zinit

contributor @ Open-source documentation (5 years)

Contributed hundreds of documentation corrections and enhancements to 400+ open source projects.
by patchnull | 2026-03-11 06:22:25+00:00
A 30K-line PR for type resolution sounds scary but this is exactly what pre-1.0 means. Rust went through multiple rounds of similar foundational rework before stabilization, remember the old sigil-based pointer syntax. Getting the type system formalized before committing to backwards compat is worth temporary churn. The real test is whether this new design makes future type system changes smaller and more incremental rather than requiring another 30K-line rewrite.
by mizmar | 2026-03-11 06:22:57+00:00
It's not that bad. You need really large files to notice.
The largest realistic file I'll ever touch - sqlite3 amalgamation with 270k lines and 9.1 kB - still takes only 6 ms to memmove it on my poor laptop.
Any regular up-to 10k lines file is memmoved in order of microseconds.
by codazoda | 2026-03-11 06:23:39+00:00
Is this an effective CTA?

What types of negative side-effects would there be from an “open source” application that was free, open, and restricted entirely by license? Rather than “we make no warranty” the license says “you may not use”.

I’ve also had a few products I’ve released on GitHub as public but never assigned a license, which probably means they are proprietary, unless GitHub TOS says otherwise.

I realize each of these is a unique situation.
by rixed | 2026-03-11 06:24:07+00:00
I solve this issue (agent looking at too much and changing too much) with the best abstraction ever invented : files and permissions.

One task is usually composed of 2 input files, a specification and a header file, and the task is to output the implementation and nothing more. Agent user has no other permissions in the file system, has no tools, just output the code that's directed into a file. I run ´make' whenever I update a specification. Token count is minimal.

Do I save time? Not much, but having to specify and argue about everything is interesting, and I trust myself that I'm not loosing any knowledge this way; be it the why or the how.
by qxxx | 2026-03-11 06:24:12+00:00
so, seniors will review now the AI slop code.. I am also doing this task and reviewing this kind of code takes time as the code is often overengineered. Code works but will have potential bugs. I am not able to find every bug or implication quickly. But I am also using ai to review the ai slop lol, because why now. After that I am also quickly reviewing by myself.
by quasimo | 2026-03-11 06:24:39+00:00
Hey HN,

I'm particularly fond of actress Rosamund Pike (裴淳华 in Chinese) even though I've barely watched any of her movies. Her Chinese name is so elegant and perfectly suits her vibe.

The night before last, it suddenly hit me: what if other foreigners also want a beautiful, meaningful Chinese name instead of the usual random pinyin garbage?

So yesterday I sat down and built SinoName in literally one day using Google AI Studio + Deepseek API.

It follows the classic 信达雅 principles (faithfulness + expressiveness + elegance):
• Accurate character breakdown
• Real pinyin + audio pronunciation
• Short cultural story behind every name

Code on GitHub, deployed on Netlify with Cloudflare CDN.

Completely free, no sign-up.

Try it here: https://sinoname.geekaa.com

Full "idea to live product in one day" story (including how 裴淳华 sparked it all):
https://geekaa.com/d/76-about-sinoname-idea-to-product-in-24...

Would love honest feedback:
- Does the cultural explanation feel respectful and accurate?
- Tech/UX thoughts?
- Drop your English name and I’ll generate one live in the comments!

Also just launched on Product Hunt if you want to check it out.
by WaxProlix | 2026-03-11 06:25:12+00:00
Post-appstore cut it's 42%, which is high but doesn't seem crazy. The unsuccessful attempts and idle piddling all need to be subsidized to allow the successes to exist in the first place, and I suspect we all know better than to undercount cloud, hosting, SRE, and staffing costs. They're all ongoing and pretty painful, and getting a shot at creating something with effectively zero downside risk (vs making a game in Godot and building/buying all of the other parts yourself or with staff) will always come with a lower upside.
by sidkshatriya | 2026-03-11 06:25:22+00:00
I am impressed by the achievements of the Zig team. I use the ghostty terminal emulator regularly -- it is built in Zig and it is super stable. It is a fantastic piece of software.

This makes me feel that the underlying technology behind Zig is solid.

But I prefer Rust over Zig. The main difference is Rust chooses a "closed world" model while Zig chooses an "open world" model: in Rust, you must explicitly implement a trait while in Zig as long as the shape fits, or the `.` on a structure member exists (for whichever type you pass in), it will work (I don't use Zig so pardon hand wavy description).

This gives Zig very powerful meta programming abilities but is a pain because you don't know what kind of type "shapes" will be used in a particular piece of code. Zig is similar to C++ templates in some respects.

This has a ripple effect everywhere. Rust generated documentation is very rich and explicit about what functions a structure supports (as each trait is explicitly enrolled and implemented). In Zig the dynamic nature of the code becomes a problem with autocomplete, documentation, LSP support, ...
by marysminefnuf | 2026-03-11 06:25:27+00:00
A couple dozen republicans can end the national nightmare by just impeaching and removing this pedophile. Like think about how its actually in their best interest to do that lol. The rising stars of the party when trump came around and they swore fealty to him are now getting aarp magazine 11 years later. They literally do not have political future beyond trump. Maybe theyre okay with that trade off if it means they can direct billions of taxpayer funds to shell companys and have steinway pianos or millions worth of crab everyday. Sorry for the rant but its so frustrating that we all want the national nightmare to end yet no one is doing anything about it.
by dnhkng | 2026-03-11 06:26:23+00:00
Cheers. I will go back though my other old projects (optogenetics, hacking Crispr/CAS9 etc), and put them on my blog.

On your questions:
1) A few other papers have been mentioned in the thread, like Solar10.7B. They duplicated the whole transformer stack, and it kinda helped. But as I found experimentally, that probably not a great idea. You are duplicating 'organs' (i.e. input processing stuff), that should only have one copy. Also, that paper didn't see immediate improvements; they had to do continued pre-training to see benefits. At that point, I'm guessing the big labs stopped bothering. Limited by hardware, I had to find unusual angles to approach this topic.

2) Nah, no more wetware for me. I did a half decade of research at a big neurobiology institute, and while it was very enjoyable, I can truly say that grant writing and paper review are 'not my thing'. This reason this info was delayed so long is that I wanted a paper in the AI field to go along with my papers in other fields. But as a Hobbyist with no official affiliation, and the attention span of a gnat, I gave up and started a blog instead. Maybe someone will cite it?
by 2001zhaozhao | 2026-03-11 06:27:52+00:00
Open-weight AI is actually analogous to closed source, free shareware you can decompile and modify yourself and run on your computer or a cloud server of your choice.

It's a clear distinction to proprietary AI, which is analogous to SaaS software controlled by a company that runs it on its own cloud, and owns your data.

But it's still not open source.
by NoPicklez | 2026-03-11 06:28:30+00:00
As someone who buys Asus motherboards when he builds PC's, it hasn't been a shock for me as an owner of a Macbook for the last 18 years.

I've been of the firm opinion for a very long time that Macbook's are the best productivity laptops and now even more so once Apple moved from Intel to their own M chips. Their entry level Macbook before the Neo you could buy and it would be a laptop that would see you for many many years.
by Duralias | 2026-03-11 06:28:31+00:00
Until we reach the point where outside becomes ruined and hostile I do not think a metaverse has much attraction to your average person, I see that as the main reason as for why VR became MR and then just AR.

Also you missed furries from your audience group, there is overlap but it is a pretty distinctive group that is actively drawn towards VR for creative expression.
by leonflexo | 2026-03-11 06:28:48+00:00
I think Bun helps with the memory pressure, granted this is relative to V8. I'd pushback on the certainty with the reality that TS provides a significant drop in entropy while benefiting from what is a sweet spot between massive corpus size and low barrier for typical problem/use-case complexity. You'll never have the fastest product with JS, but you will always have good speed to market and be able to move quickly.
by sergF | 2026-03-11 06:29:09+00:00
The transactional enqueue issue is exactly what makes me unsure about the callback model.

With in-app queues you get the nice property that the job can be created in the same DB transaction, which is harder to keep once the runner lives outside the app.

I'm trying to understand if the simplicity of an external runner is worth that trade-off for smaller apps.
by mmooss | 2026-03-11 06:32:04+00:00
> Because it's just not the world we live in. I don't care to enumerate this here.

Whether or not you enumerate it is irrelevant. Individual liberty is the foundation of the modern world. Of course there are exceptions, complications and imperfections, but the principle and the widespread practice are very clear and well established.

> sentencing

> shame

Victimhood is irresponsible (and a tactic used almost universally by the powerful to change the subject). The overwhelming amount of actual harm done between genders is men harming women, and it has been for as long as anyone can remember. That is the responsibility of men generally and the problem that needs solving. In some societies, they do address it.

If that makes you feel ashamed, that's your problem. When someone points out a problem to me, I feel responsible for solving it and get to work. 'I'm ashamed' is an obvious excuse.

> politics of power

The same behavior has very different outcomes because of differences in power between powerful and vulnerable parties: Bad behavior itself isn't dangerous, bad behavior + power is the problem.

If in a white suburb a black person says to a white person, 'we don't want your kind here', it's a bit crazy, but it's not especially dangerous. If a white person says that to a black person, that is real danger because white people have power, including government, law enforcement and the courts.

Similarly, imagining healthy, normal-sized adults, if a woman says to a man, 'I'm going to kick your ass', it's a bit threatening but not too serious. If a man says that to a women, it's very serious, dangerous to her, and he probably should be arrested.
by mock-possum | 2026-03-11 06:32:52+00:00
We feed a handful of preset questions through the new AI, we collect the results, we ask another AI to score the answers based on example ‘hood’ answers we’ve written, then we have a guy sit down and use the fallout as a starting point to rank the performance of that AI, compared to all the previous ones.

Seems like it works pretty well. Our prompts and params get tweaked towards better and better results, and we get a sense of what’s worth paying more for.
by tdeck | 2026-03-11 06:33:06+00:00
"You committed it, you own it" can't even be enforced effectively at large companies, given employee turnover and changes in team priorities and recorgs. It's hard to see how this could be done effectively in open source projects. Once the code is in there, end users will rely on it. Other code will rely on it. If the original author goes radio silent it still can't be ripped out.
by rnhmjoj | 2026-03-11 06:33:35+00:00
Well, for different reasons, but you have similar issues with IPv6 as well. If your client uses temporary addresses (most likely since they're enabled by default on most OS), OpenSSH will pick one of them over the stable address and when they're rotated the connection breaks.

For some reason, OpenSSH devs refuse to fix this issue, so I have to patch it myself:

--- a/sshconnect.c
+++ b/sshconnect.c
@@ -26,6 +26,7 @@
#include <net/if.h>
#include <netinet/in.h>
#include <arpa/inet.h>
+#include <linux/ipv6.h>

#include <ctype.h>
#include <errno.h>
@@ -370,6 +371,11 @@ ssh_create_socket(struct addrinfo *ai)
if (options.ip_qos_interactive != INT_MAX)
set_sock_tos(sock, options.ip_qos_interactive);

+ if (ai->ai_family == AF_INET6 && options.bind_address == NULL) {
+ int val = IPV6_PREFER_SRC_PUBLIC;
+ setsockopt(sock, IPPROTO_IPV6, IPV6_ADDR_PREFERENCES, &val, sizeof(val));
+ }
+
/* Bind the socket to an alternative local IP address */
if (options.bind_address == NULL && options.bind_interface == NULL)
return sock;
by dnhkng | 2026-03-11 06:34:51+00:00
Hi, thanks for the praise!

On the other papers, models like SOLAR or training a model that uses a single layers are probably going to hit a wall, based on the heatmaps I found. The transformer stack starts with randomised weights, (analogous to undifferentiated stem cells), and it seems they later form 'organs' during the trillions of pre-training tokens they undergo. My hypothesis is that you probably only want one copy of the 'token-to-thought', and 'thought-to-token' organs. It seems that you can make one layer do all three things (transforms in and out, and do the 'thinking'), but I think specialisation will always win.
by Affric | 2026-03-11 06:36:31+00:00
I have lived at high latitudes and agree; funnily enough around that time of year fog and cloud cover often meant no sun even if you were out during the day, records of 62 days with no sight of the sun. Crushing stuff.

But the situation you describe is literally not physically possible where I currently live due to proximity to the equator and being west of the line of longitude our clock runs on during standard time, but DST demands we wake up in darkness.

How the workday in the modern economy is fundamentally unjust. You shouldn't have to sign away your ability to see the sun for a job (unless at extremely high latitudes or extreme weather conditions).
by jc-myths | 2026-03-11 06:37:21+00:00
Yeah I've experimented with that pattern. The meta-agent approach works for catching obvious stuff, like "did the build pass" or "does this file actually exist." But the harder bugs are semantic. The agent writes a function that returns the right shape of data but with wrong values, or adds a fallback that masks the real failure. A supervising agent reading the same code often has the same blind spots.

What's worked better for me is building verification into the workflow itself, like explicit test assertions the agent has to pass before it can claim "done," plus a rule that any API call must show a real response, not a mock. Basically treating the AI like a junior dev who needs guard rails, not a senior who just needs a code review.
by Animats | 2026-03-11 06:37:54+00:00
Oh, that incident.

That was back when Altavista, the first search engine, was in downtown Palo Alto.
Brian Reid was behind that. It was intended as a demo for the DEC Alpha CPU. They wanted to show
that a large number of little machines could do a big job, which was a radical idea at the time.
They were leasing an old telco building, on Bryant St. behind the Walgreens on University Avenue. The telco had moved to a larger building nearby when they went from crossbar to 5ESS, leaving behind the very tall racks typical of electromagnetic central offices.

That's where the modern data center began. Before this, data centers were raised floor operations. This one was racks and racks of identical servers, with cable trays overhead. This was the first one to look like a telephone central office. Because that's what it was before.

The building is still some kind of data center. For a while, it was PAIX, the Palo Alto Internet Exchange, the peer meeting point for west coast ISPs. Equinix has it now; it's their SV8 location, offering colocation services. Small by modern standards, but close to the early HQs of many famous startups, including Facebook.

The grease problem was written up in the local newspaper, back when Palo Alto had one. Palo Alto Utilities (the city owns its power company) got the report, and quickly realized someone was dumping grease into their transformer vault. So they put someone on stakeout, watching all night. The offending restaurant employee was caught. The restaurant was fined and billed for the cleanup.

In 2006, there was another grease dumping incident in a transformer vault a block further north. This one did result in a grease fire.[1]
Palo Alto Fire Department has a CO2 truck, and dumped enough CO2 in to put out the fire. Power was out for most of the night.

I used to live within walking distance of there.

[1] https://www.paloaltoonline.com/news/2006/03/12/grease-dumpin...
by jedberg | 2026-03-11 06:40:02+00:00
> Most approaches keep the executor inside the app and use the DB for coordination

Actually the opposite. DBOS is the only one that does it that way. All the other durable execution solutions require an external coordinator.

I think your assessment is incorrect though. Using the app and DB does not mean building new infra for every project (your project already has an app and a DB). You simply import the library and it does the rest, and then you write your code normally. Every other solution requires building new, similar infrastructure for every project (the external coordinator).

The language-native library approach is the far simpler and more repeatable way to do it.

As another example, an AI with the right context can build a DBOS app, often in one shot. An AI will take a ton of work to build your terraform or YAML to set up the infra you need to use an external coordinator.

The beauty of the DBOS solution is that it works great for tiny apps and massive apps with billions of checkpoints, and everything in between.
by beanshadow | 2026-03-11 06:40:26+00:00
> it will continue to improve, but it won’t “go recursive” or whatever the claim is. It’s always been recursive.

I suspect "going recursive" often colloquially means that AI systems achieve their exponential growth without human software engineers in the mix. This is a moment whose sudden apparent nearness does justify some of the ramping rhetoric, in my opinion.
by dnhkng | 2026-03-11 06:41:21+00:00
Yes, thats true.

But that points again to the main idea: The model has learnt to transform Base64 into a form it can already use in the 'regular' thinking structures.

The alternative is that there is an entire parallel structure just for Base64, which based on my 'chats' with LLMs in that format seems implausible; it acts like the regular model.

If there is a 'translation' organ in the model, why not a math or emotion processing organs? Thats what I set out to find, and are illustrated in the heatmaps.

Also, any writing tips from the Master blogger himself? Huge fan (squeal!)
by thomasmg | 2026-03-11 06:44:36+00:00
The syntax in Kotin is: "val name: String? = getName(); if (name != null) { println(name.length) // safe: compiler knows it's not null }"
So, there is no explicit type conversion needed.

I'm arguing for integer / and %, there is no need for an explicit "non-zero integer" type: the divisor is just an integer, and the compiler need to have a prove that the value is not zero. For places where panic is fine, there could be a method that explicitly panics in case of zero.

I agree an annotation / effect system would be useful, where you can mark sections of the code "panic-free" or "safe" in some sense. But "safe" has many flavors: array-out-of-bounds, division by zero, stack overflow, out-of-memory, endless loop. Ada SPARK allows to prove absence of runtime errors using "pragma annotate". Also Dafny, Lean have similar features (in Lean you can give a prove).

> I think it's fine for division to be exposed as a panicking operation by default

That might be true. I think division (by non-constants) is not very common, but it would be good to analyze this in more detail, maybe by analyzing a large codebase... Division by zero does cause issues sometimes, and so the question is, how much of a problem is it if you disallow unchecked division, versus the problems if you don't check.
by bob1029 | 2026-03-11 06:44:38+00:00
> The trick is not to play zero sum games. This is what I have been saying the whole time. Go create value for others and don’t worry about the returns.

This strategy is highly effective but it's also difficult to tolerate as an ordinary advanced ape. Watching others play less noble games and obtain easier wins can be discouraging over time.

I have found that the less you care about money the easier it is to acquire. Risk aversion, greed and interpersonal drama will kill a good idea way before anything else. I sometimes like to reframe this one as "100% of $0 is still $0".
by menaerus | 2026-03-11 06:45:16+00:00
M4 is 38 TOPS at INT8 precision whereas SpacemiT K3 is 60 TOPS at INT4 precision so at best they would be equal in "AI" performance but they are not because the rest of the K3 chip is much less capable than M4 (as I would expect).

E.g. M4 total system memory bandwidth is 120GB/s whereas K4 is 51GB/s, single core memory bandwidth is 100-120GB/s vs ~30GB/s. M4 has 10 CPU cores and neural engine with 16 cores whereas K3 has 8 CPU cores and 8 "AI" cores, K3 clock frequency is almost half the clock frequency in M4 etc. etc.

But anyway thanks for sharing, always good to learn about new hardware.