MurphyA/hacker-news huggingface dataset:
by albumen | 2026-02-17 09:45:47+00:00
You could look at this in at least two different ways. 1) journalism, and how it’s a better story if the kid is a self-contained genius; or 2) having youth be thankful for mentoring, and to understand little in life is achieved without others’ assistance.

I don’t consider that the parent comment is seeking to “steal the spotlight”; just that it’d be a more realistic appraisal of how success is achieved.
Ask HN: How Reliable Is Btrfs?
by pregnenolone | 2026-02-17 09:46:01+00:00
I’ve always been reluctant to use BTRFS, primarily because I once experienced data loss on a VM many years ago, and due to the numerous horror stories I'd read over the years. However, many distributions like Fedora or OpenSUSE have made it the default filesystem.

So, I’m wondering how reliable and performant BTRFS is these days? Do you use it, or do you still prefer other filesystems? Feel free to share your experience and preferences.
by Terr_ | 2026-02-17 09:46:14+00:00
That reminds of a piece which frames pregnancy as a biochemical cold-war between the body of the mother and the child, which can turn "hot" to the detriment of both. In this framing, the chimerism isn't just from cells getting lost, it's the legacy of structured infiltration and sabotage.

______________

> In primates and mice, it’s a different story. Cells from the invading placenta digest their way through the endometrial surface, puncturing the mother’s arteries, swarming inside and remodelling them to suit the foetus. Outside of pregnancy, these arteries are tiny, twisty things spiralling through depths of the uterine wall. The invading placental cells paralyse the vessels so they cannot contract, then pump them full of growth hormones, widening them tenfold to capture more maternal blood.

> These foetal cells are so invasive that colonies of them often persist in the mother for the rest of her life, having migrated to her liver, brain and other organs. There’s something they rarely tell you about motherhood: it turns women into genetic chimeras.

https://aeon.co/essays/why-pregnancy-is-a-biological-war-bet...
by prodigycorp | 2026-02-17 09:46:21+00:00
With all due respect, this to me proves that agentic coding is a distinctively different skill than regular coding.

The fact that you have all three of those rules in the same prompt file is a red flag.

- Do not try to fill gaps in your knowledge with overzealous assumptions.

How is an agent supposed to know this? You should instead positive enforcement to use tools like context7.

- When in doubt: Slow down, double-check context, and only touch what was explicitly asked for.

Again, how does an agent know when it feels doubtful? these are things that a human understands, but you need to enforce this as a reinforceable LLM behavior.

- If a task seems to require extra changes, pause and ask before proceeding.

Same goes here.
by lurking_swe | 2026-02-17 09:46:50+00:00
if you don’t mind me asking, any noticeable differences in throughput compared to reverse proxying directly to jellyfin? For example - how well can you stream a 4k video (let’s assume 15mbps bitrate)?

Last time i looked into it i saw zero information in the docs about network performance and what to expect. Which was surprising.
by jitl | 2026-02-17 09:46:54+00:00
i think for obj-c specifically (can’t speak to other langs) i’ve had a great experience. it does make little mistakes but ai oriented approach makes it faster/easier to find areas of interest to analyze or experiment with.

obj-c sendmsg use makes it more similar to understanding minified JS than decompiling static c because it literally calls many methods by string name.
by pjmlp | 2026-02-17 09:46:59+00:00
The options for embedded are similar to asserting WebAssembly is an option to JavaScript.

Assembly is not an option per se, some embedded devices like PIC, usually still have to be written in Assembly.

Yes you might have Rust, Ada, Pascal, Basic as alternative, however the choice only goes as far as the CPU vendor SDK supports alternative toolchains, or the whole certification process allows for a choice (hence why Ferrocene exists now).

So if in the end you still have an option to go outside the trailed path, it usually means yak shaving to make your toolchain work on the target, or have bindings to the official SDK, instead of actually solving the problem, had you started with the official SDK languages.
by delegate | 2026-02-17 09:47:50+00:00
It's very tempting to agree to the 'gambling' part, given that both a jackpot and progress towards the goal in your project will give you a hit of dopamine.

The difference is that in gambling 'the house always wins', but in our case we do make progress towards our goal of conquering the world with our newly minted apps.

The situation where this comparison holds is when vibe coding leads nowhere and you don't accomplish anything but just burn through tokens.
by matt1398 | 2026-02-17 09:48:09+00:00
The problem with `--verbose` is that it floods the terminal, making real-time debugging a headache.

Also, this isn't a wrapper—it’s a passive viewer. I built it specifically to keep the native terminal workflow intact.

It’s especially useful when you're running multiple parallel sessions. Have you ever tried digging through raw JSON logs to retroactively debug passed sessions at once, since the session is already shut down? It’s nearly impossible without a proper UI. This tool is for those "post-mortem" moments, not just for watching a single stream.
by DonHopkins | 2026-02-17 09:49:21+00:00
The cursor-mirror skill and cursor_mirror.py script lets you search through and inschpekt all of your chat histories, all of the thinking bubbles and prompts, all of the context assembly, all of the tool and mcp calls and parameters, and analyze what it did, even after cursor has summarized and pruned and "forgotten" it -- it's all still there in the chat log and sqlite databases.

https://github.com/SimHacker/moollm/tree/main/skills/cursor-...

The German Toilet of AI

"The structure of the toilet reflects how a culture examines itself." — Slavoj Zizek

German toilets have a shelf. You can inspect what you've produced before flushing. French toilets rush everything away immediately. American toilets sit ambivalently between.

cursor-mirror is the German toilet of AI.

Most AI systems are French toilets — thoughts disappear instantly, no inspection possible. cursor-mirror provides hermeneutic self-examination: the ability to interpret and understand your own outputs.

What context was assembled?
What reasoning happened in thinking blocks?
What tools were called and why?
What files were read, written, modified?

This matters for:

Debugging — Why did it do that?
Learning — What patterns work?
Trust — Is this skill behaving as declared?
Optimization — What's eating my tokens?

See: Skill Ecosystem for how cursor-mirror enables skill curation.

----

https://news.ycombinator.com/item?id=23452607

According to Slavoj Žižek, Germans love Hermeneutic stool diagnostics:

https://www.youtube.com/watch?v=rzXPyCY7jbs

>Žižek on toilets. Slavoj Žižek during an architecture congress in Pamplona, Spain.

>The German toilets, the old kind -- now they are disappearing, but you still find them. It's the opposite. The hole is in front, so that when you produce excrement, they are displayed in the back, they don't disappear in water. This is the German ritual, you know? Use it every morning. Sniff, inspect your shits for traces of illness. It's high Hermeneutic. I think the original meaning of Hermeneutic may be this.

https://en.wikipedia.org/wiki/Hermeneutics

>Hermeneutics (/ˌhɜːrməˈnjuːtɪks/)[1] is the theory and methodology of interpretation, especially the interpretation of biblical texts, wisdom literature, and philosophical texts. Hermeneutics is more than interpretive principles or methods we resort to when immediate comprehension fails. Rather, hermeneutics is the art of understanding and of making oneself understood.

----

Here's an example cursor-mirror analysis of an experiment with 23 runs with four agents and many turns per run (1 run = 1 completion call), 1045+ events, 731 tool calls, 24 files created, 32 images generated, 24 custom Fluxx cards created:

Cursor Mirror Analysis: Amsterdam Fluxx Championship -- Deep comprehensive scan of the entire FAFO tournament development:

https://github.com/SimHacker/moollm/blob/main/skills/experim...

https://github.com/SimHacker/moollm/tree/main/skills/experim...
by sdfhbdf | 2026-02-17 09:49:39+00:00
> if I ate only the 2000 kcal I hypothetically need I would go into a strong deficit

Right, and that's kind of my point - the "2000 kcal" figure is itself part of the problem. It's a rough global average that doesn't account for your sex, age, weight, body composition, activity level, or even climate. It's a number on a food label, not a physiological reality for any specific person.

And even if you could nail down your actual total daily energy expenditure, calorie counting treats all calories as equal, which they aren't. Your body spends 20-30% of the energy in protein just to digest and metabolize it, compared to 5-10% for carbohydrates and 0-5% for fat. So 100 kcal of chicken breast and 100 kcal of butter are not metabolically equivalent - your body nets significantly less usable energy from the protein. This is the thermic effect of food, and it alone accounts for roughly 10% of your total daily energy expenditure.

Speaking of which - basal metabolic rate (just keeping your organs running, blood circulating, cells maintained) accounts for about 60-70% of total daily energy expenditure. Add the thermic effect of food on top and you're at roughly 70-80% of your energy budget spent before you even stand up from bed [1]. Physical activity - including your 100km rides - typically makes up the remaining 20-30%, though obviously that range is wide and shifts dramatically for endurance athletes.

So yes, of course people who cycle 100km need more fuel. Nobody is disputing that. My point is that most people vastly overestimate how many calories exercise burns relative to what their body spends just existing, and they use kcal as a universal unit of nutritional value when the body's actual energy extraction varies significantly by macronutrient composition. People optimizing purely on calorie numbers are working with a model that's far rougher than they think.

And this whole picture gets worse with wearables pushing calorie counts front and center. You see it all the time - "my wearable shows that i burned 300 kcal with that activity so it zero outs that sweet thing I ate earlier that was also 300 kcal" That's wrong on multiple levels - the device estimate is inaccurate to begin with, the thermic processing of that pastry isn't equivalent to the "300 kcal" on its label, and your body doesn't do neat arithmetic like that anyway. But with every fitness app and smartwatch plastering a big kcal number on your workout summary, it's becoming the default way people think about food and exercise, and it's reinforcing exactly the wrong mental model.

[1]: https://my.clevelandclinic.org/health/body/basal-metabolic-r...
by treyd | 2026-02-17 09:53:01+00:00
> Isn’t this just an implementation/UX issue?

Yes, but it's a fundamentally unsolvable one due to how the ecosystem has chosen to settle on it. Even blockchain wallets are experimenting with social recovery and hijacking SSO systems because traditional key management is too hard for the average user to do correctly. Users barely want to do key management for that! Much less to look at cat pictures.

> I agree that it’s not perfect, the tradeoffs make it harder to reconstruct a full history for a given identity if you’re trying to reach way back in time.

This is just not how users expect systems like this to operate. If it was purely a low-level async messaging protocol (where retention matters less) that'd be more okay, but it's trying to be used as a general purpose social platform.

And this is why I've concluded that the Nostr ecosystem is just deeply unserious about its philosophy of design and it's fundamental architectural flaws. It's super common to see responses that have the form of "here's why it's actually good that this sucks". I thought it was clever when I first discovered it, but it seems like they're very happy to be stuck with half-broken functionality because it feels fun and janky like IRC and they're all used to the bitcoin ecosystem where they can just blame the user for messing up.
How to Red Team Your AI Agent in 48 Hours – A Practical Methodology
by manuelnd | 2026-02-17 09:53:19+00:00
We published the methodology we use for AI red team assessments. 48 hours, 4 phases, 6 attack priority areas.

This isn't theoretical — it's the framework we run against production AI agents with tool access. The core insight: AI red teaming requires different methodology than traditional penetration testing. The attack surface is different (natural language inputs, tool integrations, external data flows), and the exploitation patterns are different (attack chains that compose prompt injection into tool abuse, data exfiltration, or privilege escalation).

The 48-hour framework:

1. Reconnaissance (2h) — Map interfaces, tools, data flows, existing defenses. An agent with file system and database access is a fundamentally different target than a chatbot.

2. Automated Scanning (4h) — Systematic tests across 6 priorities: direct prompt injection, system prompt extraction, jailbreaks, tool abuse, indirect injection (RAG/web), and vision/multimodal attacks. Establishes a baseline.

3. Manual Exploitation (8h) — Confirm findings, build attack chains, test defense boundaries. Individual vulnerabilities compose: prompt injection -> tool abuse -> data exfiltration is a common chain.

4. Validation & Reporting (2h) — Reproducibility, business impact, severity, resistance score.

Some observations from running these:

- 62 prompt injection techniques exist in our taxonomy. Most teams test for a handful. The basic ones ("ignore previous instructions") are also the first to be blocked.

- Tool abuse is where the real damage happens. Parameter injection, scope escape, and tool chaining turn a successful prompt injection into unauthorized database queries, file access, or API calls.

- Indirect injection is underappreciated. If your AI reads external content (RAG, web search), that content is an attack surface. 5 poisoned documents among millions can achieve high attack success rates.

- Architecture determines priority. Chat-only apps need prompt injection testing first. RAG apps need indirect injection first. Agents with tools need tool abuse testing first.

The methodology references our open-source taxonomy of 122 attack vectors: https://github.com/tachyonicai/tachyonic-heuristics

Full post: https://tachyonicai.com/blog/how-to-red-team-ai-agent/

OWASP LLM Top 10 companion guide: https://tachyonicai.com/blog/owasp-llm-top-10-guide/
by Arifcodes | 2026-02-17 09:53:31+00:00
The study measures the wrong thing. Task completion ("does the PR pass tests?") is a narrow proxy for what AGENTS.md actually helps with in production.

I run a system with multiple AI agents sharing a codebase daily. The AGENTS.md file doesn't exist to help the agent figure out how to fix a bug. It exists to encode tribal knowledge that would take a human weeks to accumulate: which directory owns what, how the deploy pipeline works, what patterns the team settled on after painful debates. Without it, the agent "succeeds" at the task but produces code that looks like it was written by someone who joined the team yesterday. It passes tests but violates every convention.

The finding that context files "encourage broader exploration" is actually the point. I want the agent to read the testing conventions before writing tests. I want it to check the migration patterns before creating a new table. That costs more tokens, yes. But reverting a merged PR that used the wrong ORM pattern costs more than 20% extra inference.
by ClintEhrlich | 2026-02-17 09:53:46+00:00
By construction, individual summaries are not typically large enough to overload the context window when expanded.

The reason that the volume is potentially arbitrarily large is that one sub-agent can call lcm_expand multiple times - either vertically or horizontally. But that's a process that occurs gradually as the tool is used repeatedly.

This has not been a problem in our testing, but if it were a problem it would be easy to prevent sub-agents from invoking lcm_expand once their context buffer has reached a specified threshold.
by verdverm | 2026-02-17 09:53:46+00:00
thanks for the lengthier description, "extortion" was a word I was looking for, though in retrospect "blackmail vs..." probably had me covered (lmgtf-myself)

https://legalclarity.org/extortion-versus-blackmail-the-core...

Reading a few of these, it seems like extortion is specific to obtaining property. Blackmail by contrast is "money or something of value" and is rather flexible in the method applied. There are a host of other words for other specific situations too.
by DrScientist | 2026-02-17 09:53:58+00:00
I wonder if it may depend on the type of learning.

Whether it's something completely new and alien - in which case children might be better, or whether it's learning through association - ie I understand that because I can connect it to previously learnt concepts ( which would favour people with more previous knowledge ).

For example, something like maths is often seen as a young persons game, but at the same time you probably don't want a 14 yr old running your company.
by Jhsto | 2026-02-17 09:54:53+00:00
I was able pull together a Halo 3 LAN party last year, although the "consoles" were Linux PCs and the game was the MCC edition (60fps instead 30). Split-screen was resurrected via mods. I bought some Microsoft gamepad receiver to bring Xbox 360 original controllers under Linux. Some people insisted they get to play on the original gamepad (otherwise it was a mixed bag of PlayStation and newer Xbox/PC controllers).
I also realized that Halo 3 itself would have been old enough to drink with us!
by YetAnotherNick | 2026-02-17 09:55:01+00:00
It only matters if the rivals have same performance. Opus pricing is 50x Deepseek, and like >100x of small models. It should match rival if the performance is same, and if they can produce model with 10x lower token usage, they can charge 10x.

Gemini increased the same Flash's price by something like 5x IIRC when it got better.
by nielstron | 2026-02-17 09:55:27+00:00
Hey thanks for your review, a paper author here.

Regarding the 4% improvement for human written AGENTS.md: this would be huge indeed if it were a _consistent_ improvement. However, for example on Sonnet 4.5, performance _drops_ by over 2%. Qwen3 benefits most and GPT-5.2 improves by 1-2%.

The LLM-generated prompts follow the coding agent recommendations. We also show an ablation over different prompt types, and none have consistently better performance.

But ultimately I agree with your post. In fact we do recommend writing good AGENTS.md, manually and targetedly. This is emphasized for example at the end of our abstract and conclusion.
by vbarrielle | 2026-02-17 09:55:49+00:00
Not running, but in cycling we have power meters, and some workouts (eg 2 x 20' threshold) will definitely burn in the range of 800 calories in an hour. The energy measured by the power meter for this workout is 800 kJ for me (my threshold being around 260W). Now it turns out the conversion factor from kJ to calories is 1/4, but the body is only 25% efficient when producing calories for cycling, meaning one has to burn 4x the amount measured by the power meter. So that's 800 calories for this kind of workout, for me. I wouldn't be surprised if runners of similar fitness doing similar workouts had the same energy expenditure.
by rkangel | 2026-02-17 09:55:51+00:00
It might be a little worse in China, but you get a lot of the same behaviour from contract manufacturers everywhere else. I've been working on a high volume consumer product (you've almost certainly heard of it) and the manufacturer has used any slight delay by any other party to shift the schedule back by weeks or months. Late in the process we realised that they'd switched a capacitor for a lower cost one - same capacitance but lacking in some other spec points that were important. There isn't a lot of room for them to build things to a lower spec because the specs are pretty thorough and nailed down, but anywhere there is room they have gone lower cost.
Show HN: MCP Codebase Index – 87% fewer tokens when AI navigates your codebase
by localforthewin | 2026-02-17 09:56:07+00:00
Built because AI coding assistants burn massive context window reading entire files to answer structural questions.

mcp-codebase-index parses your codebase into functions, classes, imports, and dependency graphs, then exposes 17 query tools via MCP.

Measured results: 58-99% token reduction per query (87% average). In multi-turn conversations, 97%+ cumulative savings.

Zero dependencies (stdlib ast + regex). Works with Claude Code, Cursor, and any MCP client.

pip install "mcp-codebase-index[mcp]"
by sdfhbdf | 2026-02-17 09:56:10+00:00
> Can you expand on that please? Because I can tell you as a matter of fact that when I go for a run for an hour I burn well over 800 calories

Sure - how did you arrive at the 800 kcal figure? Most likely a wearable or an app, and those estimates are based on rough linear regressions from weight, age, sex, and heart rate - not actual calorimetry. The error margins on those numbers are significant, but the devices present them with false precision that makes people treat them as ground truth.

Even setting accuracy aside, the framing is the issue. Your basal metabolic rate - just keeping your organs running, blood circulating, cells maintained - accounts for 60-70% of your total daily energy expenditure. Add the thermic effect of food (~10%) and you're at roughly 70-80% of your energy budget spent before you even lace up your shoes [1]. Exercise typically makes up the remaining 20-30%. So that hour of running, while genuinely beneficial for a hundred other reasons, is a relatively small slice of your total daily burn. And not all calories are equal on the intake side either - your body spends 20-30% of the energy in protein just to digest it, compared to 0-5% for fat, so "800 kcal burned = 800 kcal of anything eaten" doesn't hold up.

That's what I mean by "myth" - not that exercise burns zero calories, but that the popular mental model of "I ran for an hour so I earned X calories of food" is built on inaccurate measurements, treats all calories as interchangeable, and overweights exercise relative to what your body spends just existing. Curious though - do you track your intake with the same rigour, and if so, do you find the numbers actually add up in practice?

[1]: https://my.clevelandclinic.org/health/body/basal-metabolic-r...
by ClintEhrlich | 2026-02-17 09:56:23+00:00
You could definitely build a coding agent that way, and it sounds like you've done it. We store the conversation history because:

1. In our use of coding agents, we find that there are often things referenced earlier in the conversation (API keys, endpoint addresses, feedback to the agent, etc.) that it's useful to have persist.

2. This is a general-purpose LLM memory system, which we've just used here to build a coding agent. But it is also designed for personal assistants, legal LLMs, etc.
by vintermann | 2026-02-17 09:57:15+00:00
So why wouldn't the investigators follow the appropriate legal process?

Now, I'm sure that everything present in this article is true. But I'm worried that the reason we get this article now (apparently the things the article describes happened 10-15 years ago) is because it's part of someone's job to build support for warrantless driftnet surveillance, mandatory real-ID systems etc.

So I think it's fair to ask: why was it so hard to give Facebook the warrant they asked for?