MurphyA/hacker-news huggingface dataset:
by throwup238 | 2026-02-19 17:15:15+00:00
> I'm finding it surprisingly hard to find sources for known age of species - is that information collected somewhere? Or is it something we often just don't know because of how sparse the fossil record is?

It depends on what you mean by the age of the species. You can find the oldest known fossil occurrence at the Paleobiology Database [1] and the divergence time from molecular phylogenies via TimeTree [2].

[1] https://paleobiodb.org/

[2] https://timetree.org/
by anthomtb | 2026-02-19 17:15:20+00:00
I have the exact same problem. 6'0 (183.5 cm) height, 160lbs (72kgs), and a long torso. Mediums and even smalls are ideal for my shoulders and chest but it's a crapshoot if they will keep my belly button covered through a normal range of motion. I don't want to bare it and can't imagine anyone wants to see it.

Tentrees, Fox and Prana are the brands I've found that consistently fit my upper body.
by PaulHoule | 2026-02-19 17:15:22+00:00
As long as I've been involved with marketing I think most people have always underestimated the amount of work to break through indifference. If it came to putting up flyers for instance, my idea of a marketing plan is 50-1000 flyers with 5-15 distinct designs, the average young person thinks that people are so desperate to hear your message that 10 flyers will do it.

Back in the day for everything that "went viral" there were 100 "normal dudes" who had a marketing plan that involved "going viral" but it didn't happen.

People are about to learn in the AI age that: (1) if you speed up execution or the appearance of execution you run into the difficultly of marketing more quickly, and (2) Putting "AI" in your subject line will just make people close their earflaps the same way that "NFT" did two years ago.

Sure it is tuff and most people won't succeed, but with hustle and imagination some people are going to make it today, but the "life hacks" that work are either not generalizable (you are not going to transform into a fox) or trade secrets.
by giraffe_lady | 2026-02-19 17:15:36+00:00
I don't think it's posturing, I think they legitimately do want the entire world to have access to the CSAM, nonconsensual generated porn, and white nationalist propaganda now being produced and disseminated by american tech companies. These are sincere values of the american right and they have dedicated significant resources to them already. This isn't out of nowhere and it isn't fake either.
by hoyhoy | 2026-02-19 17:15:36+00:00
I looked at trying to implement -fbounds-safety and -Wunsafe-buffer on a reasonably large codebase (4,000 C and C++ files), and it's basically impossible.

You have to instrument every single file. It can be done in stages though. Just turn the flag on one-by-one for each file. The xnu kernel is _mostly_ instrumented with -fbounds-safety.
by conductr | 2026-02-19 17:16:08+00:00
It’s how people unwind from stressful day, just like doomscrolling. But most of these people aren’t considered alcoholics by society, it’s fairly normal behavior until it affects other parts of your life. From what I can tell anyways. I also don’t drink much so don’t get it when people need to have a glass of wine or whatever after a completely normal day.
by shivekkhurana | 2026-02-19 17:16:17+00:00
The TTS/STT models are actually good and aggressively priced. I personally built a voice-mode ai assistant.

STT time to first token is ~300ms. ~20 second audio takes less than 1 second to be converted.

TTS time to first token is ~700ms. ~20 second of audio is generated under 2 seconds.
by antonvs | 2026-02-19 17:16:22+00:00
Yes, but the issue is that they don't have identity. The idea of assigning unique identifiers to particles is doomed because, basically, "there are no particles, there are only fields" (https://arxiv.org/abs/1204.4616).

Particles are how quantized fields present themselves when probed by localized interactions. In general, they're also observer-dependent.

The idea of assigning an "ID" to an object reflects a macro-level notion of re-identifiable objects persisting through time. But at the quantum level, that kind of classical individuality - object identity - doesn't exist.
by whynotminot | 2026-02-19 17:16:25+00:00
The hallucination rate with the Gemini family has always been my problem with them. Over the last year they’ve made a lot of progress catching the Gemini models up to/near the frontier in general capability and intelligence, but they still felt very late 2024 in terms of hallucination rate.

Which made the Gemini models untrustworthy for anything remotely serious, at least in my eyes. If they’ve fixed this or at least significantly improved, that would be a big deal.
by sergiomattei | 2026-02-19 17:16:30+00:00
I don't know. LLMs are great at writing code; but you have to have the right ideas to get decent output.

I spend tons of time handholding LLMs--they're not a replacement for thinking. If you give them a closed-loop problem where it's easy to experiment and check for correctness, then sure. But many problems are open-loop where there's no clear benchmark.

LLMs are powerful if you have the right ideas. Input = output. Otherwise you get slop that breaks often and barely gets the job done, full of hallucinations and incorrect reasoning. Because they can't think for you.
I built two Loihi-parity neuromorphic processors from scratch
by catalyst-neuro | 2026-02-19 17:16:33+00:00
Catalyst — two generations of neuromorphic processor, built solo, from RTL to SDK to cloud API.

Catalyst N1 (first generation — Loihi 1 feature parity):
- 128 cores, 1,024 CUBA LIF neurons each, 131K CSR synapses per core
- Programmable microcode learning engine: 16 registers, 14 opcodes (STDP, three-factor eligibility, homeostatic normalization)
- 8-bit graded spike payloads (actually a Loihi 2 feature — N1 exceeds Loihi 1 here)
- 3 synapse formats (sparse, dense, population-coded), per-synapse delays up to 63 timesteps
- Triple RV32IMF RISC-V embedded cluster with FPU
- 24-bit state precision (vs Loihi 1's 23-bit)
- 25 RTL testbenches, 98 scenarios, zero failures

Catalyst N2 (second generation — Loihi 2 feature parity):
Everything in N1, plus:
- Programmable neuron microcode engine — each neuron runs a user-defined program, replacing the fixed CUBA datapath
- 5 neuron models shipped: CUBA LIF, Izhikevich (4 presets), Adaptive LIF, Sigma-Delta, Resonate-and-Fire
- 4 graded spike payload formats (0/8/16/24-bit vs Loihi 2's limited range)
- Variable-precision weight packing: 1/2/4/8/16-bit (vs Loihi 2's 1-8 bit — we exceed here)
- 5 spike traces (x1, x2, y1, y2, y3) enabling triplet STDP
- Per-synapse-group plasticity enable (30-70% learning speedup)
- Persistent reward traces with temporal credit assignment
- Homeostatic threshold plasticity
- Full observability: performance counters, 25-variable state probes, energy metering
- Convolutional synapse encoding (4th format)
- 28/28 FPGA integration tests on AWS F2 (VU47P), 163K+ spikes, zero mismatches
- Hardware-accurate simulation defaults (24-bit fixed-point, strict SRAM limits)

The SDK covers both processors: 3,091 tests, 155 features, CPU/GPU/FPGA backends. 85.9% on Spiking Heidelberg Digits (surrogate gradient trained, 16-bit quantized).

The big architectural decision in N2 was making the neuron programmable. N1 had a hardcoded CUBA LIF datapath — functional but limiting. N2 replaces it with a fetch-execute microcode engine where each neuron runs its own program from instruction SRAM. Same philosophy
as the shift from fixed-function GPU pixel pipelines to programmable shaders. You can now implement arbitrary neuron dynamics without changing hardware.

Cloud API lets you try both processors without any hardware:

- Cloud API: https://catalyst-neuromorphic.com/cloud
- GitHub: https://github.com/Mr-wabbit/catalyst-neurocore
- Full SDK source access: https://github.com/sponsors/Mr-wabbit (from $25/mo)
- Licensed under BSL 1.1 (source-available, free for research)
- Contact: henry@catalyst-neuromorphic.com

238 development phases, all by one person. Happy to answer questions about either processor.
by bubblewand | 2026-02-19 17:16:45+00:00
If you follow the fairly common path of "various expensive, intermittent medical problems for a couple decades, a handful of years of very-bad medical problems, nursing home, then hospice care" in the US, and you don't have a shitload of money, you don't really have a savings of your own, you're just temporarily taking care of the medical and end-of-life-care industries' money. There's not going to be much to pass down.

This becomes more true by the year, as those costs keep rising faster than broader inflation.
by adrian_b | 2026-02-19 17:17:33+00:00
For a long time AMD has been offering much better FP64 performance than NVIDIA, in their CDNA GPUs (which continue the older AMD GCN ISA, instead of resembling the RDNA used in gaming GPUs).

Nevertheless, the AMD GPUs continue to have their old problems, weak software support, so-and-so documentation, software incompatibility with the cheap GPUs that could be used directly by a programmer for developing applications.

There is a promise that AMD will eventually unify the ISA of their "datacenter" and gaming GPUs, like NVIDIA has always done, but it in unclear when this will happen.

Thus they are a solution only for big companies or government agencies.
by zozbot234 | 2026-02-19 17:18:16+00:00
> does not use "separate government funding".

Yes, it does. The Obama administration explicitly appropriated general government funds to try and make up a developing shortfall in the 'fund'. There is no money being accumulated because there are more payouts than taxes - but even if that wasn't the case, these are not actual "bonds" that have been bought on any market, they're just non-market government obligations.
by autoexec | 2026-02-19 17:18:18+00:00
> virtually all Singaporeans believe they own their houses, and it is a point of pride and financial security. Most houses are on 99 year leases

It's not as if the US fairs any better. Here you never own your home, you have to pay property taxes your whole life (basically rent) or it will be taken from you. Eminent domain means that they can take your property from you at any time even if you've kept paying them.

Singapore has a home ownership rate of ~90% vs 65% in the US and prices are so unaffordable that on average people buying their first starter homes in the US are in their 40s! Most Americans, if they're lucky will get maybe get 30 years of their life in a home they own while they are still young and healthy enough to enjoy it.

Last I heard Singapore only had about 1,000 homeless. Whatever they're doing with housing could probably be improved on, but they seem to be doing a lot better than we are.
by Night_Thastus | 2026-02-19 17:18:27+00:00
Personally, as someone in C and C++ for the last few years, memory access is almost never the root bug. It's almost always logic errors. Not accounting for all paths, not handling edge cases, not being able to handle certain combinations of user or file input, etc.

Occasionally an out-of-bounds access pops up, but they're generally so blindingly obvious and easy to fix that it's never been the slow part of bug fixing.
by WarmWash | 2026-02-19 17:18:27+00:00
I'm pretty sure it writes comments for itself, not for the user. I always let the models comment as much as they want, because I feel it makes the context more robust, especially when cycling contexts often to keep them fresh.

There is a tradeoff though, as comments do consumer context. But I tend to pretty liberally dispense of instances and start with a fresh window.
by davidguetta | 2026-02-19 17:18:43+00:00
Implementation and Sustainability
Hardware: Gemini 3 Pro was trained using Google’s Tensor Processing Units (TPUs). TPUs are
specically designed to handle the massive computations involved in training LLMs and can speed up
training considerably compared to CPUs. TPUs often come with large amounts of high-bandwidth
memory, allowing for the handling of large models and batch sizes during training, which can lead to
better model quality. TPU Pods (large clusters of TPUs) also provide a scalable solution for handling the
growing complexity of large foundation models. Training can be distributed across multiple TPU devices
for faster and more efficient processing.

So google doesn't use NVIDIA GPUs at all ?
by danans | 2026-02-19 17:19:44+00:00
> The "tax" is the delta between what the government pays out for the bonds vs what a bond of equivalent risk in the free market would have paid.

It also robs the individual's freedom to gamble with their retirement funds while expecting/demanding a bailout when shit hits the fan.

In the USA we have thoughtful policies that allow people over a certain amount of wealth invested in key industries to do that.
Show HN: Run SigNoz on ObsessionDB and ClickHouse Cloud
by alvarogar | 2026-02-19 17:20:14+00:00
Hey HN, I'm Alvaro. I work on ObsessionDB, a managed ClickHouse service. We run petabyte-scale ClickHouse infra as a service.

We open-sourced a fork of SigNoz's schema migrator that makes SigNoz work on SharedMergeTree. That means it runs on both ObsessionDB and ClickHouse Cloud.

The problem: SigNoz's production setup assumes sharded ClickHouse. The official docs spec 56 CPU cores, 152 GiB RAM and 10 nodes including 3 ZooKeeper instances. Your observability tool becomes its own ops problem.

SigNoz's schema creates `_local` tables (ReplicatedMergeTree) and `Distributed` tables on top. Our migrator does three things:
- Creates the `distributed_*` tables as SharedMergeTree (this is where data actually lives)
- Creates the local table names as VIEWs pointing to the SharedMergeTree tables
- Redirects Materialized Views to write to SharedMergeTree directly, since VIEWs can't receive inserts

SigNoz doesn't know the difference. The Query Service reads from the same table names. The OTEL Collector writes to the same table names. No core code changes.

This works with ClickHouse Cloud too, not just us. Same docker compose, different credentials.

Repo: https://github.com/obsessiondb/signoz-obsessiondb
Blog post with the full technical walkthrough: https://obsessiondb.com/blog/scale-signoz-on-obsessiondb

What other ClickHouse-backed tools would benefit from SharedMergeTree support? Would love to hear what you're running into.
by rodrigorcs | 2026-02-19 17:20:15+00:00
> I don't really see what problem this solves. If you have proper timeouts and circuit breakers in your service this shouldn't really matter.

Each service discovering by their own is not really the main problem to be solved with my proposal, the thing is that by doing it locally, we lack observability and there is no way to act on them.

> what we done is to create flag where we put the % value we want to bring back

Oh I see, well that is indeed a good problem to solve. Openfuse does not do that gradual recovery but it would be possible to add.

Do you think that by having that feature and having the Openfuse solution self-hosted, it would be something you would give a try? Not trying to sell you anything, just gathering feedback so I can learn from the discussion.

By the way, if you don't mind, how often do you have to run that type of recovery?
by xaviervn | 2026-02-19 17:20:22+00:00
Me too, and I'm glad to see that this point keeps being brought up. I noticed that what shapes my satisfaction (or dissatisfaction) about working with AI depends on whether have understanding of what's being built or not.

For a prototype, it's pretty amazing to generate a working app with one or two prompts. But when I get serious about it, it becomes such a chore. The little papercuts start adding up, I lose speed as I deal with them, and the inner workings of the app becomes a foreign entity to me.

It's counterintuitive, but what's helping me enjoy coding is actually going slower with AI. I found out that my productivity gains are not on building faster, but learning faster and in a very targeted way.
by Towaway69 | 2026-02-19 17:21:42+00:00
Agree with the quality level but there are other languages where that is also the case: Erlang for example is probably one of those languages.

> Elixir's relationship to Ruby is pretty overstated

Perhaps I am actually am over thinking this. Elixir has probably diverged enough from Ruby (e.g. defmodule, pipe operators, :atom syntax) for LLMs to notice the difference between the two. But it does open the question, though, how does an LLM actually recognise the difference in code blocks in its training data.

There are probably many more programming languages where similarities exist.
by myvoiceismypass | 2026-02-19 17:21:59+00:00
18F was founded during the Obama administration, so absolutely no one in MAGA-world will ever have considered it apolitical.

One of the many fired IGs last year was investigating Neurolink - so noone in Musk-world will have considered them apolitical either.

Nothing government-related or government-adjacent will ever be broadly accepted as apolitical again in the US, regardless of intent or truth. Its very depressing.
by lucafabbri | 2026-02-19 17:22:00+00:00
Hi everyone, I've built BLite because I am using LiteDB for years in my professional works, but lately the maintainer seems disappeared and I was stuck with a dependency that doesn't work with the NativeAOT.

So I decided to rewrite a database from scratch. It is a BSON document store but with zero reflection because I used the Source Generators for the serialization. This makes the binaries very small and fast. I also want to be honest and say that I used the AI for helping me in some parts of the library development and for the implementation.

I also implemented the "C-BSON" (Compressed BSON) to save some space by mapping the field names to a global dictionary, so the documents are smaller but still standard BSON. In my benchmarks on .NET 10, it is around 8x faster than SQLite on the single inserts.

It supports B-Tree, Hash, and R-Tree indexes, plus ACID transactions and an experimental vector search. I tried to keep the API very simple, similar to EF Core, for not having a big learning curve.

I am very happy of the result and I'd love to hear what the HN community thinks about the C-BSON idea or the Source Generator implementation.

Happy to answer to any technical questions!
by CuriouslyC | 2026-02-19 17:22:33+00:00
You can remove a lot of toil from the writing process without taking away a writer's ability to do line edits. There's a lot of outlining, organization, bookkeeping and continuity work AI automates in the early draft/structural editing process.

Most writers can't even get a first draft of anything done, and labor under the mistaken assumption that a first draft is just a few minor edits away from being the final book. The reality is that a first draft might be 10% of the total time of the book, and you will do many rounds of rereading and major structural revision, then several rounds of line editing. AI is bad at line editing (though it's ok at finding things to nitpick), so even if your first draft and rough structural changes are 100% AI, you have basically a 0% chance of getting published unless you completely re-write it as part of the editing process.
by embedding-shape | 2026-02-19 17:22:43+00:00
At first when I got started with using LLMs I read/analyzed benchmarks, looked at what example prompts people used and so on, but many times, a new model does best at the benchmark, and you think it'll be better, but then in real work, it completely drops the ball. Since then I've stopped even reading benchmarks, I don't care an iota about them, they always seem more misdirected than helpful.

Today I have my own private benchmarks, with tests I run myself, with private test cases I refuse to share publicly. These have been built up during the last 1/1.5 years, whenever I find something that my current model struggles with, then it becomes a new test case to include in the benchmark.

Nowadays it's as easy as `just bench $provider $model` and it runs my benchmarks against it, and I get a score that actually reflects what I use the models for, and it feels like it more or less matches with actually using the models. I recommend people who use LLMs for serious work to try the same approach, and stop relying on public benchmarks that (seemingly) are all gamed by now.
by alephnerd | 2026-02-19 17:23:10+00:00
Absolutely! The TTS/STT approach that Sarvam and the other Indian firms are taking is more intuitive for a larger share of people and usecases. The "replace an SDR" or "replace a call-center" usecase is such an easy win to show POV.

I feel this is also why you don't see the same degree of hype as you would with the other players. When you are taking an application-driven approach to launching AI products, hype matters less than targeting decisionmakers and showing that your product directly aligns with their outcomes.
by MarioMan | 2026-02-19 17:23:31+00:00
If you have evidence to the contrary, it would make for a great lawsuit. Apple is very explicit that they cannot read it. This data is end-to-end encrypted, much like the data collected by the Health app. They never have the keys to it.

In comparison, Apple also has plenty of your data “encrypted at rest”, where they have the keys (unless you use advanced data protection). That data is only superficially secured. That’s not what this feature uses.
by inglor_cz | 2026-02-19 17:23:57+00:00
I enjoy one specific fact about programming with Claude.

My work often entails tweaking, fixing, extending of some fairly complex products and libraries, and AI will explain various internal mechanisms and logic of those products to me while producing the necessary artifacts.

Sure my resulting understanding is shallow, but shallow precedes deep, and without an AI "tutor", the exploration would be a lot more frustrating and hit-and-miss.
by bor_real | 2026-02-19 17:23:59+00:00
The main reason I use Discord is the users on the platform. I am locked into it via the network effect.

I will say, I tried out Stoat when it was still called Revolt, and it seems to be good enough to replace Discord for many common use-cases.

It could be worth it, but only if the people you talk to on Discord follow you.
by justin66 | 2026-02-19 17:24:17+00:00
> I went through school in the 90s and was 100% not taught any method involving phonics or sounding out words.

I mean... would you remember? Pretty much the only thing I remember about the relevant early years of kindergarten and elementary school is the time I went to school sick and threw up on my desk. (then again, I'm old)

> The problem was in fact the reverse of what you're claiming, whole language was brought in by consultants selling a curriculum. This is also easily confirmed.

I actually have no idea how to confirm that, but I'm sort of willing to take your word for it. By contrast, it's easy to find the phonics lesson products that are sold to parents, like the one I mentioned in my post. Such products used to be pumped on the ads during AM hate radio shows, among other things. Those products were complimentary to the "educators are terrible and public education is terrible and everything done by experts is terrible" message those shows pushed.

To be clear, I don't think those lessons are necessarily useless. Time spent with kids outside class on their education is a positive thing.

> Implementation varied state to state

I'm sure that accounts for a lot of the difference in lesson plans we're talking about, and some states emphasize strong local control as well.
by soiltype | 2026-02-19 17:24:33+00:00
As a rule, "pop history" is full of shit and is probably better considered misinformation than anything else. I probably don't I know of a single general-audience history/anthropology book that doesn't horrify scholars of the field.

As unfortunate as it is, studying cause-and-effect is extremely complex. If it's even theoretically possible to distill it down to easily digestible ideas, that's well outside our current technical capabilities.

There's usually going to be some true and interesting information in these books, but it will be too deeply embedded in a narrative that is misleading.