MurphyA/hacker-news huggingface dataset:
by umairnadeem123 | 2026-02-24 05:30:49+00:00
one pattern that works well in practice: combine passive health checks with circuit breaker state machines at the client level. instead of binary healthy/unhealthy, track a sliding window of error rates per backend. once a backend crosses your error threshold, move it to half-open state where it gets 1 in N requests as probes. this gives you sub-second detection without the false-positive problem of aggressive active health checks.

the article focuses on detection speed but misses the equally important problem of recovery speed. backends that come back after a failure often get thundering-herded by all the clients that simultaneously notice the recovery. connection ramping (slowly increasing traffic to a recovered backend) is just as important as fast detection.
by exodust | 2026-02-24 05:30:52+00:00
> "culture shift is necessary"

Is it though? When older sibling helps younger sibling with "accessing Steam", or something reasonable like that, even the most sensible and thoughtful older sibling won't be interested in "culture shifts" that block gaming fun.

The alcohol and seatbelt analogies try to elevate equivalence, but miss the mark by a lot. Even one drop of alcohol is obviously not suitable for underage. No seatbelt increases risk no matter your age. "Social media" exposure for the young person is often completely fine and full of "young person" content and activity.
by notepad0x90 | 2026-02-24 05:30:58+00:00
Can an argument be bade that CSS only exists becuase javascript failed to develop a styling component to displace it?

I like to think webassembly is the right track. But ECMAScript and CSS alike need(ed) to devolve into a simpler byte-code like intermediary language syntax.

Browsers supporting complex languages has always been a bad idea, what they need to support is capabilities, and access and security primitives. wasm hasn't displaced javascript, because afaik, the wasm spec for browsers doesn't require them to implement javascript (and ideally, CSS) via wasm.

Instead of distilling, simplifying and speccing CSS and Javascript, browsers caked on layers upon layers of complicated features. The idea that browsers should define and regulate the languages developers use to write front-end code needs to die.
by umairnadeem123 | 2026-02-24 05:31:46+00:00
the interesting design choice here is prioritizing robustness over minimalism. most tiny lisps cut corners on error handling and GC to hit a small line count, which makes them great demos but terrible for embedding in real systems. femtolisps approach of being small AND robust enough to use as julias actual parser for years is a much harder target to hit.

the fact that its still under 10k lines of C after all this time while supporting a real type system, proper tail calls, and a copying GC is impressive. for comparison, lua - often cited as the gold standard for embeddable languages - is around 30k lines.
by jolks | 2026-02-24 05:31:56+00:00
I pre-configured it to use https://github.com/modelcontextprotocol/servers/tree/main/sr... which stores it in a JSON file, memory.json. However, user can freely configure it to use FalkorDB using https://github.com/FalkorDB/FalkorDB-MCPServer. Feel free to submit issue on github so I can support you better https://github.com/jolks/goto-assistant/issues/new
by umairnadeem123 | 2026-02-24 05:32:24+00:00
the biggest gap in current agent security thinking is the lack of standardized capability scoping. right now every agent framework invents its own permission model. we need something like OAuth scopes but for agent actions - a common vocabulary for "can read files but not write", "can call APIs but not spend money", "can draft emails but not send".

the drone registration analogy in the RFI is actually quite apt. for agents that can take real-world actions (deploy code, make purchases, send communications), some kind of capability manifest that can be audited before deployment would go a long way. the hard part is that agents are compositional - agent A calling agent B calling a tool creates permission chains that are hard to reason about statically.
by leptons | 2026-02-24 05:33:00+00:00
I've worked at so many places in my career that "not understanding code" is not an excuse. It is a skill to be able to read and follow code and get up to speed quickly, even on shit codebases. But "AI" generated code makes that so much more difficult, and the "AI" isn't going to walk you through it, and neither will your new coworkers. We aren't in a race to the bottom with "AI", we're in a speedrun to the bottom, and I don't think it's going to end up going too well for whatever developers are left in a few years.
by umairnadeem123 | 2026-02-24 05:33:04+00:00
one thing these benchmarks dont capture well is the GC pressure difference at scale. in a long-running service processing millions of requests, the allocation pattern matters more than the per-op cost. xsyncs 24 B/op per write adds up to real GC pauses when you have hundreds of goroutines hitting the map concurrently.

in practice ive found that for most go services the bottleneck isnt the map implementation at all - its the serialization of the values going in and out. if youre storing anything more complex than a string or int, the json/protobuf marshal dominates the profile. worth profiling your actual workload before optimizing the map.
by umairnadeem123 | 2026-02-24 05:33:51+00:00
the real cost isnt compute or bandwidth - its the URL frontier management. at a billion pages you need a deduplication layer that can handle hundreds of thousands of URL lookups per second while also tracking crawl-delay per domain, last-visit timestamps, and priority scoring. thats essentially a specialized database problem.

bloom filters work for basic dedup but they cant handle recrawl scheduling. we ended up using a combination of rocksdb for the frontier state and an in-memory hash ring for domain-level rate limiting. the frontier alone consumed more engineering time than the actual HTTP client.
by notepad0x90 | 2026-02-24 05:34:13+00:00
You're probably right, no disagreement there. but in the context of my previous comment, the people that write cobol today, I don't think there is a lot of work for them trying to reverse engineer native code back to cobol because the source is lost. But you make a really good point, if AI can assist with lost code recovery, perhaps it will assist them in migrating away from it or getting rid of workarounds and complexities implemented to get that previously opaque binary's behavior.
by icfly2 | 2026-02-24 05:34:25+00:00
The Shah fell after an ever increasing wave of student protests and violent crack downs.

The regime is doing better to hold on to power by killing more brutally, but there is no guarantee that will suffice to motivate its soldiers to kill the protesters in sufficient numbers to quell the unrest.

Ask Assad how killing his people worked out.

Just to be clear, I’m obviously against killing protesters.
by umairnadeem123 | 2026-02-24 05:34:35+00:00
the docker-based reproducibility angle is really smart for academic agri-robotics. the biggest complaint i hear from researchers trying to replicate field robot experiments is that the software environment is always subtly different - different ROS versions, different CUDA, different model weights. if you can hand someone a docker compose and a BOM and they get identical behavior, that alone justifies the project.

for the vision pipeline, have you considered running inference on the ESP32-S3 directly for simple green-on-brown detection? YOLO is overkill for basic weed/crop discrimination and the latency of shipping frames over ethernet to the A55 adds up when you want real-time control of the weeding actuator. tflite micro on the ESP32 can do simple binary classification at 30+ fps.
by ericd | 2026-02-24 05:35:04+00:00
Well, it might actually decrease GDP in this case, because it's making it so I can just quickly make products that I would've otherwise purchased. But it's also made me more productive, and purchasing things isn't good for its own sake. So maybe measuring progress via GDP isn't ideal?

The thing I'm making with the APIs is very helpful to me, maybe it'll be helpful to others, who knows.
by umairnadeem123 | 2026-02-24 05:35:14+00:00
whats striking about this timeline is the compression of release cycles. in 2020-2021, a new frontier model was a once-a-year event that the whole field would analyze for months. now its roughly weekly. the practical consequence for builders is that you cant optimize for any specific models quirks anymore - your architecture needs to be model-agnostic with a swappable inference layer.

the open vs closed filter is the most useful feature here. the real story of 2024-2025 is the gap between frontier closed models and frontier open models shrinking from about 18 months to maybe 3-4 months. that changes the economics of production deployments completely.
by dw_arthur | 2026-02-24 05:36:08+00:00
It should show in decreased revenue for the company you didn't buy the product from. It also should show up at your company either as increased profit margin, increased investment, increase in total employee wages, or increased dividend payout.

If this is happening on a widespread basis in the economy we should see evidence of it sometime this year and that's what investors are anticipating with SaaS stocks.
by jballanc | 2026-02-24 05:36:16+00:00
When I was a young kid, my mother was a “stay at home mom”, which meant that she babysat the kids of 5 or 6 of the other families in our neighborhood where both parents worked. For me, it was a wonderful experience growing up having a ready-made group of close friends and my mother close at hand. It did mean that my mother effectively sacrificed her career (though she eventually went to work for my father as his office manager and was instrumental to his success), but I’m certain she was not charging $20k/yr/kid (or whatever the equivalent in 1980s dollars would be).

What Americans seem to only just now be waking up to is that lack of work/life balance, lack of family leave accommodations, and loss of community has a very real, very tangible dollar amount cost. I’m very, very tired of the knee-jerk response to every “socialist” proposal being, “yeah, that’s great, but how are you going to pay for it?”

How are you going to pay for not having family leave? How are you going to pay for not having universal healthcare? How are you going to pay for not having tuition-free college for all? These choices have a cost, and Americans are paying that cost every day!
by umairnadeem123 | 2026-02-24 05:36:22+00:00
the biggest gap in most compiler courses is the backend. you spend weeks on parsing and type checking, then the instructor hand-waves over register allocation and instruction selection as "out of scope." but thats where most of the interesting engineering problems live - the NP-hard graph coloring, the phase ordering problem, the tension between optimization and compile time.

interactive visualizations would help enormously here. the cranelift or LLVM IR transformations are nearly impossible to reason about from text dumps alone. being able to step through an optimization pass and see how it rewrites the IR graph would make concepts like common subexpression elimination or loop invariant code motion actually intuitive instead of abstract.
by dpc_01234 | 2026-02-24 05:36:47+00:00
Do everyone realize, that it's easier than ever was to build p2p software? E.g. You can use Pkarr as a distributed identity system, and Iroh for reliable p2p connectivity.

There's no point in messing with custom hardware, etc. We could host bunch of redundant p2p access points for everyone as portable software.

E.g. I'm building a P2P/F2F Social Media protocol that is very close to syndication platform. https://app.radicle.xyz/nodes/radicle.dpc.pw/rad:zzK566qFsZn... . I'm not saying that it's exactly the same thing as author is looking for, but the technical bits and even functionality are very close.
by umairnadeem123 | 2026-02-24 05:37:01+00:00
unikernels have been "almost ready" for production for a decade now, and the core blocker hasnt changed: debuggability. when your application IS the kernel, a segfault doesnt give you a nice stack trace - it gives you a triple fault and a reboot. the operational tooling gap between linux containers and unikernels is still enormous.

that said, the serverless use case might finally be the right fit. cold start times of single-digit milliseconds and attack surfaces measured in thousands of lines instead of millions are exactly what lambda-style workloads need. if you can accept that debugging means "redeploy with more logging" rather than "attach gdb," unikernels start looking very attractive for short-lived stateless functions.
by umairnadeem123 | 2026-02-24 05:37:36+00:00
this solves a real problem. i run coding agents that have access to my workspace and the .env files are always the scariest part. even with .gitignore, the agent can still read them and potentially include secrets in context that gets sent to an API.

the approach of encrypting at rest and only decrypting into environment variables at runtime means the agent never sees the raw secrets even if it reads every file in the project. much better than the current best practice of just hoping your .gitignore is correct and your AI tool respects it.

one suggestion: it would be useful to have a "dry run" mode that shows which env vars would be set without actually setting them. helps verify the config is correct before you realize three services are broken because a typo in the key name.
by handoflixue | 2026-02-24 05:37:49+00:00
Testing some subset X does not mean the test is rigged unless they failed to disclose that.

But also:

GPT 5.2 Thinking, Standard Effort: Walk - https://chatgpt.com/share/699d38cb-e560-8012-8986-d27428de8a...

I'm assuming "GPT 5.2 Thinking" is, in fact, a thinking model?
by danpalmer | 2026-02-24 05:37:56+00:00
LLMs using code to answer questions is nothing new, it's why the "how many Rs in strawberry" question doesn't trip them up anymore, because they can write a few lines of Python to answer it, run that, and return the answer.

Mathematica / Wolfram Language as the basis for this isn't bad (it's arguably late), because it's a highly integrated system with, in theory, a lot of consistency. It should work well.

That said, has it been designed for sandboxing? A core requirement of this "CAG" is sandboxing requirements. Python isn't great for that, but it's possible due to the significant effort put in by many over years. Does Wolfram Language have that same level? As it's proprietary, it's at a disadvantage, as any sandboxing technology would have to be developed by Wolfram Research, not the community.
by umairnadeem123 | 2026-02-24 05:38:11+00:00
this is a great teaching resource. building an agent from scratch really demystifies whats happening inside tools like claude code. the core loop is surprisingly simple - its just read prompt, call LLM with tools, execute tool calls, loop. all the complexity is in the details: how you manage context window limits, how you handle tool errors gracefully, and how you prevent the agent from going in circles.

one thing worth adding as a phase 8 or bonus: context window management. the naive approach of appending every tool result to the conversation history blows up fast when the agent reads large files. implementing summarization or truncation strategies is where most real-world agent engineering time goes.
by Silhouette | 2026-02-24 05:38:30+00:00
Market forces have not been sufficient because the funny money moving around in the AI sector is starting to significantly damage other parts of the economy. I'm not sure how I can say this any more clearly.

If the AI sector wants to take a big risk that either pays off or it doesn't then that's one thing. If the danger is contained and the people taking the risks are the only ones who will suffer if things don't work out then that's their choice. The important point is that the danger in this case is not contained. It's not just the AI sector being affected now. It's not just the people choosing to take the risk in the hope of a big profit who are being harmed.

I don't think it's realistic any longer to treat this as some sort of short term supply-and-demand problem with luxury products. It's been building up for several years already. We're seeing public statements from some of the big companies involved that already run through 2026 and beyond that imply the supply chain situation continuing to worsen for everyone else. And the components in question are now necessary for a lot of normal operations and day-to-day life.

If we just wait and see for another 2-3 years as some are suggesting then in the meantime real businesses who were operating responsibly and doing nothing wrong will be failing to grow as they should, putting up prices for their customers, letting staff go, or even failing completely. Governments will be redirecting tax revenues to basic IT infrastructure instead of public services. People who just wanted to buy a normal device for their own personal use won't be able to afford one or maybe to find one at all and their quality of life will fall.

I can see no reason why we should allow these harmful outcomes just because some already rich VCs and some AI tech bros are playing games at the scale of the global economy to try to sustain the unrealistic valuations of the big tech companies for their own benefit. It's increasingly unlikely to work anyway and it's a realistic possibility that the bubble will burst this year so trying to contain the fallout from that as much as possible instead of allowing the bubble to inflate even further is not a bad idea either.
by hattmall | 2026-02-24 05:39:51+00:00
It just seems really obvious to me that it's not one disease. One problem with the research is that there is SO much money. It's corrupting. There's a whole thing about the plaque cartel and if you aren't testing around a possibly flawed concept the availability of funds is much lower.

I just feel the thinking is off, it's like we are trying to treat cuts by removing scabs and scar tissue. We really need deep investigation on the sources, which I feel in many cases are industrial chemicals and how some people's body / immune system respond to them.

One of the most compelling studies I saw was how distance from a Golf Course predicted neurodegenerative diseases, based on their use of certain pesticides.
by vintermann | 2026-02-24 05:40:43+00:00
They were not that much more sparse than their neighbors. They typically moved with the seasons, so maybe they needed a little more space (a summer place and a winter place at the very least), but not radically more so. After all, the Norse had summer pastures in the mountains too.

Reindeer herding is younger than people think, as I said. Until the major predators were exterminated, it wouldn't have been possible to have herds of the sizes we're used to. Land wasn't the limiting factor for herd size. Until eastern Norwegian immigrants came in the 1750s on, and settled inland - a crazy thing to do according to locals since of course you wanted to live by the sea where the fish, the money were, and it was cold inland - there wasn't much land use conflict.

There were no centrally organized assimilation efforts until von Westen at the earliest. And while he was zealous about rooting out superstition and customs which he saw as pagan, he was also protecting the language, teaching people in their own language (for a generation after Westen, Sami were said to be more educated than other northerners, and you see it in the censuses!) and certainly wasn't putting people into encomiendas or otherwise forcing them to change their material way of life.
by Foobar8568 | 2026-02-24 05:40:54+00:00
I rewrote two times my personal tool, one to rust, and out of rust with LLM, I mainly "vibe code" for reasons but the tool is more complete and less buggy than most B2B and enterprise business or IT software that I have seen in 20years at different fortune 100.

I feel the effort and frustration to have parity with LLM is more than doing a full rewrite without.
by geocar | 2026-02-24 05:41:41+00:00
> We can't say that a function equals a set

Why not?

Can we not so easily speak of the set of all inputs and the set of all outputs? Why not exactly then is a function not a set of morphisms/arrows?

To me, x->x+1 and {(x,x+1)|x∈R} seem the same[1]

Because it seems useful to be able to make statements of the cardinality of that set: If there are a lot of rules, then that set is big, but if there are few rules (like x->x+1), that set is small. This is enough to permit some analysis.

It also preserves "plus" for sets, because a function plus a function is the sum of those rules being considered.

What is it do you think I am missing?

[1]: I understand I don't really mean big-R here because computers have limited precision for fadd/add circuits, so if you'd prefer I said something slightly differently there please imagine I did so.
by impl | 2026-02-24 05:41:59+00:00
This website is at least 25 years old. It uses one of the pre-canned templates from FrontPage 2000. Cut 'em a little slack. :-)

This was obviously someone's research project, and some of the papers have example code in them, e.g. see https://www.cs.cornell.edu/talc/papers/talx86-wcsss.pdf.
by fortzi | 2026-02-24 05:43:37+00:00
It sounds crazy to me, and indeed very disturbing if it was true. However if it was true, I would have known at least one person to fit the image they’re portraying. I know none.

I tried to find the original publication, but all I found was press coverage. Specifically I’m interested in the demographics - who was asked these questions, aside from the religious attributes that they name.
by rand42 | 2026-02-24 05:43:50+00:00
> "Obviously, you need to drive. The car needs to be at the car wash."

Actually, this isn't as "obvious" as it seems—it’s a classic case of contextual bias.

We only view these answers as "wrong" because we reflexively fill in missing data with our own personal experiences. For example:

- You might be parked 50m away and simply hand the keys to an attendant.

- The car might already be at the station for detailing, and you are just now authorizing the wash.

This highlights a data insufficiency problem, not necessarily a logic failure. Human "common sense" relies on non-verbal inputs and situational awareness that the prompt doesn't provide. If you polled 100 people, you’d likely find that their "obvious" answers shift based on their local culture (valet vs. self-service) or immediate surroundings.

LLMs operate on probabilistic patterns within their training data. In that sense, their answers aren't "wrong"—they are simply reflecting a different set of statistical likelihoods. The "failure" here isn't the AI's logic, but the human assumption that there is only one universal "correct" context.
by ajainvivek | 2026-02-24 05:44:51+00:00
Your finding #2 is the most important — hallucinations are a retrieval problem, not a generation problem. We hit the same wall building legal document retrieval at Brainfish. Swapping embedding models gave incremental gains, but the real jump came from preserving document hierarchy (articles, sections, clauses) during retrieval instead of flattening everything into chunks.

Curious, did you evaluate any structure-aware retrieval approaches on this benchmark, or purely embedding-based?