MurphyA/hacker-news huggingface dataset:
by npinsker | 2026-03-08 12:51:39+00:00
Sorry to hear you’re having a hard time — it sounds rough. Hope things get better soon.

Not related, but one thing that’s comforted me in the past is that one’s brain physiologically responds to these changes like an addiction withdrawal — and one has to endure a cocktail of hormones and awful feelings but those will subside, with time, even if you make no changes to your life at all.

It goes without saying that some new groups to socialize with would help, but that’s a lot easier said than done :) It’s also important for you to believe — emotionally, not logically — that things will get better. This is difficult. Depression can lead to black-and-white thinking in areas that it doesn’t belong, so I feel it’s essential to combat this if it’s something you’re seeing. One thing that helps a lot is trying to have your expectations violated in a pleasant way.

Specifically — rather than tunneling on a hobby you’re interested in, I think it’s surprisingly valuable to join a group around something you think you might kinda like, but don’t like too much. (D&D would be an example for me.) Things will rarely turn out the way you expect, and I think if you go in with a lower bar for success, you leave more room for yourself to be pleasantly surprised and feeling hopeful and open.
by masklinn | 2026-03-08 12:52:36+00:00
> The problem is that it is lagging behind enough that it is falling out of the support window for a lot of libraries.

Which is a concern for those libraries, I've not seen one thread criticising (or even discussing) numpy's decision.

> Should tooling give a big fat warning about it being essentially unusable if you try to use it with the 2026 Python ecosystem? Also yes.

But it's not, and either way that has nothing to do with uv, it has to do with people who use pypy and the libraries they want to use.
by gizmodo59 | 2026-03-08 12:53:17+00:00
Unfortunately the paper doesn’t include gpt 5.3 which was released around the same time as opus 4.6 and also gpt 5.4 few days back. Both are available via api

https://developers.openai.com/api/docs/models/gpt-5.3-codex

IMHO The harness must be used when running these experiments. The model vendors know best on giving the best harness with gpt 5.4 and codex or Claude code with opus 4.6 which makes a big difference if you are running any kind of agentic coding tasks.

I see both Claude and gpt to be neck and neck in coding. Every other model+harness is definitely 3-6 months behind. Right now codex seems to be the best in terms of solving complex bugs, long running tasks, much higher limits and even speed while Claude seems to do well in front end and their cli ux seems nice! Codex app is very good though (wish it wasn’t electron as a memory hog but it’s good)
by mikkupikku | 2026-03-08 12:53:19+00:00
I don't know if it'll be a year or two, hard to say exactly when the AI bubble will pop, but I feel quite certain it's coming. The AI stuff is great but most of the money being thrown around to all these different companies is mostly going to be wasted. Investors don't know who the winners and losers will be, just like when people were investing in pets.com instead of amazon.com.
by manoDev | 2026-03-08 12:54:32+00:00
My understanding is that you're describing the CIE 1931 color space:

https://en.wikipedia.org/wiki/CIE_1931_color_space#Color_vis...

You'll see it is based on the physics and human biology, and is the basis for everything else.

The thing is, this color space isn't very useful for color calculations in the perceptual/subjective sense (e.g. if I just want to change one characteristic, like luminosity, without affecting the chromaticity), so we have transformations to more useful spaces like XYZ, Lab, etc.
Show HN: CCP, a CLI proxy that trims terminal output without changing commands
by SuppieRK | 2026-03-08 12:54:38+00:00
I built ccp after noticing how much context coding agents waste on terminal output.

It runs the real command, trims the repetitive parts, and leaves command behavior alone.

Two real receipts from actual work:

Java/Gradle task (Claude Code): 5,330,571 -> 90,127 estimated tokens across 88 commands (98.31% saved)
Research task across 4 repositories (Claude Code): 944,007 -> 59,195 estimated tokens across 96 commands (93.73% saved)
You can try it with the install script, run ccp init, then check the effect with ccp gain.

It is not a magic win on every command. In this repo's own usage with Codex, overall savings were lower when the mix was heavy on passthrough-heavy commands like sed and openspec.

It also works across a broad set of agents through ccp init, including Codex, Claude, Cline, Cursor, Gemini, GitHub Copilot, Roo Code, Aider, and others.

Repo: https://github.com/SuppieRK/ccp

If you use coding agents heavily, which commands waste the most context in your workflow?
Show HN: Trawl – Scrape any site with natural language fields, not CSS selectors
by trawlcli | 2026-03-08 12:55:07+00:00
Every scraper I've written has the same failure mode: it works for three months, a site redesigns, and my CSS selectors silently return empty strings. The data is still right there on the page — a human can find it instantly — but the scraper is blind.

Trawl fixes this by splitting the problem. You describe what you want:

trawl "https://books.toscrape.com" --fields "title, price, rating, in_stock"

The LLM (Claude) looks at one sample item and derives a full extraction strategy — CSS selectors, attribute mappings, type coercion, fallback selectors. That strategy gets cached. Every subsequent page with the same structure is extracted with pure Go + goquery. No API calls, no token cost, full concurrency.

The key insight: LLMs are good at understanding HTML structure, but you don't need them to extract 10,000 rows. Use AI for intelligence, Go for throughput.

When a site redesigns, the structural fingerprint changes, the cache misses, and trawl re-derives automatically.

You can preview exactly what it figured out:

$ trawl "https://example.com/products" --fields "name, price" --plan

Strategy for https://example.com/products
Item selector: div.product-card
Fields:
name: h2.product-title -> text (string)
price: span.price -> text -> parse_price (float)
Confidence: 0.95

Some things that took real engineering effort:

- JS-rendered SPAs: headless browser with DOM stability detection — polls until element count stabilizes and skeleton loaders resolve, scrolls to trigger lazy loading, auto-clicks "Show more" buttons
- Multi-section pages: detects candidate data regions heuristically, target a specific section with --query "Market Share", scopes extraction via container selectors
- Self-healing: monitors extraction health (% of fields populated), re-derives the strategy if it drops below 70%
- Iframes: auto-detects and extracts from iframes when they contain richer data than the outer page

Output is JSON, JSONL, CSV, or Parquet. Pipes cleanly:

trawl "https://example.com/products" --fields "name, price" --format jsonl | jq 'select(.price > 50)'

Written in Go. MIT licensed.
by luckylion | 2026-03-08 12:55:08+00:00
We're very boring, our stack is PHP/postgres/mysql. A lot of Symfony, a lot of Symfony-style-code on top of Wordpress (mentioning that usually puts people off but it's all PHP in the end, and you can choose to write clean code on either).

Lots of people see PHP in general as a dead end career-wise and WP specifically as almost an insult, so there aren't many that advanced their skills and have continued to work with PHP (or Wordpress, but I believe that an experienced PHP developer has no trouble picking up WP).

We're generally very neutral on how someone arrived where they are, we don't require certificates or degrees, we focus on experience and skills. I wouldn't hire someone who isn't experienced with at least one side of our stack though (unless they're extremely good) because it takes time from other developers to upskill them and that's the one resource we don't have.

I won't disclose where I work though as that would dox myself and I much prefer anonymity.
by stratos123 | 2026-03-08 12:55:16+00:00
Base models don't write like that. This appears during RLHF. It's not totally clear why*, but probably a large part of the answer is that this style looks great to human reviewers, and only starts looking terrible once you get to play around with the released model and realise it talks like that all the time.

* The technical term is "mode collapse", see [1][2]

[1] https://en.wikipedia.org/wiki/Mode_collapse

[2] https://gwern.net/doc/reinforcement-learning/preference-lear...
Show HN: Ttylag – A userspace PTY wrapper to simulate slow/laggy terminals
by cbrunnkvist | 2026-03-08 12:55:30+00:00
If you've ever wanted to expose your fancy-schmanzy TUI to some chaos engineering and see how it handles 500ms jitter, without actually having to find a bad Wi-Fi spot, I built a tool for you!

ttylag wraps any command in a "shaped" PTY. No difficult piping, no tc queue disciplines required. It handles RTT, jitter, it lags in both directions and even has a --bits-per-byte flag (if you're into that sort of thing).

It's userspace-only, works on macOS and probably Linux too without any messing with network namespaces or firewall rules.
by clsia | 2026-03-08 12:56:08+00:00
Hi HN,
I built a small experiment to collect a longitudinal dataset of Gemini's stock predictions to study how LLMs behave over time under uncertainty.

Because the runs were captured live, this dataset is time-locked and cannot be recreated retroactively.

For ~38 days, a cronjob generated daily forecasts for ~30 stocks with 10-day horizons, keeping the prompt and parameters entirely fixed.
Each run logs the predicted price, a natural-language rationale, sentiment, and self-reported confidence.

The goal is not to create a trading system, but to study forecast stability, narrative drift, and confidence calibration in LLMs.

The main link goes to the raw Hugging Face dataset (note: actual historical prices are left for the user to rehydrate due to financial data licensing).

If you prefer to explore the data and plots interactively without downloading the CSV, I built a simple MVP here:
Dashboard:
https://glassballai.com/dashboard

Some early stats:
Stocks with most trend matches: ADBE (29/38), ISRG (28/39)
Stocks with most trend misses: AMGN (31/38), TXN (28/38)

I’d love to hear critiques on the methodology or ideas on what signals to look for in the narrative drift!
Why most general-purpose Agents fail and why I'm avoiding LLM "reasoning"
by cid435 | 2026-03-08 12:56:42+00:00
An Agent's core capability comes entirely from the underlying LLM. Therefore, the future of Agents is strictly dictated by the present state of LLMs.

So, where exactly are LLMs right now?

I believe we are currently in the "cottage industry" (or subsistence) phase of AI—the very dawn of industrialization. To use a historical analogy: we just invented the first steam engines. They are bulky, stationary, and only good for pumping water out of coal mines. We aren't anywhere close to having steam locomotives yet.

Right now, there's a massive explosion of custom Agents being built. But if you look closely, they are almost entirely "self-sufficient" and siloed. Everyone is building their own Agent for their own specific use case, but it's incredibly hard to adapt or scale them for broader use. It’s like every household having its own loom, weaving its own cloth, and never using anyone else's.

Why is this happening? It comes down to the current limits of LLMs. If we put aside multimodal capabilities for a moment, text-based LLMs basically have four core pillars:

Natural Language Understanding (NLU)

Natural Language Generation (NLG)

Tool Calling

Reasoning

The first three are already highly mature and reliable. But the fourth—Reasoning—is still an absolute minefield of hallucinations.

Yet, what do Agent developers obsess over the most? Reasoning. Why? Because it looks cool on a demo. This obsession is exactly why we can't truly "industrialize" Agents yet. It’s why it is so damn hard to find a genuinely reliable, general-purpose Agent in the wild (the recent hype and reality check around Manus is a textbook example of this).

Sure, one day LLM reasoning capabilities might surpass 99% of humanity. When that day comes, we will finally see truly powerful, general-purpose Agents. But honestly, nobody knows exactly when that timeline will hit.

My takeaway: If I am building a general Agent for production today, I am strictly utilizing NLU, NLG, and Tool Calling. I am staying the hell away from relying on "Reasoning."

A recent convo with some friends about AI got me thinking. My take seemed to resonate with them, so I’m sharing it here to hear your thoughts
by devilkin | 2026-03-08 12:56:58+00:00
I have a coworker who is obsessed by LLMs and keeps reiterating that he is super productive with them.

Yet I have to still see the first delivery or codebase by that same person. (I am not his manager)

I lean in the LLM skeptic camp, I know they're great for some things (never to outsource your thinking, what unfortunately a lot of people do), but I'd like to see some studies. Because there are a lot of net negatives in the business press, or max up to 10% improvement.
by rami61614 | 2026-03-08 12:57:11+00:00
One thing I’ve noticed is that kids often become interested in science when everyday situations connect to scientific ideas.
For example, a child might ask why the basement is colder than upstairs or why condensation forms on a windshield. Those moments can turn into short conversations that connect real experiences to scientific ideas.
I wrote a short article with examples of how these conversations can happen. I’m curious whether other parents or teachers have seen similar moments of curiosity with kids.
by ericb | 2026-03-08 12:57:21+00:00
> biased to hiring a slightly worse applicant

I understand your reasoning, but in practicality, I don't think this is true. This would be true if companies though with a coherent set of incentives. Instead, individual incentives are at-play here.

If a company is paying for a recruiter, it usually means:

- It isn't highly cash constrained
- Values the time of its IC's, managers and HR more than the fee
- Valuation for the role is not cost-based, but value-based
- Only at the penny pinching startup stage is the recruiter fee a real factor in a multi-year investment that should be yielding a high return. Beyond that, the bias evaporates and the real incentives lie with individual incentives, and available budgets.
by nagi_builds | 2026-03-08 12:57:53+00:00
Hey HN. I've been building SkyClaw for the past few months — it's a runtime for running LLM-powered agents over chat channels (Telegram, Discord, Slack) with a focus on reliability and not wasting tokens.

The problem I kept hitting with other agent setups: they're fragile. Process dies mid-task, you lose everything. The model burns through context repeating the same failed approach. Token budgets aren't managed, so you hit limits on complex tasks and the conversation just breaks.

Here's how SkyClaw handles these:

Crash recovery: After every tool round, the full session state gets checkpointed to SQLite. If the process dies, it resumes from the last checkpoint. Tasks that can't complete during graceful shutdown get saved automatically.

Token budgeting: A context manager allocates tokens across 7 priority tiers — system prompt and tool definitions always stay, recent messages get a guaranteed window, memory search is capped at 15%, cross-task learnings at 5%, and older history fills whatever's left. When messages get dropped, a summary gets injected so the model doesn't lose the thread.

Self-correction: A failure tracker counts consecutive failures per tool. After 2 failures on the same tool, it injects a prompt telling the model to try a fundamentally different approach instead of retrying the same thing.
Cross-task learning: After a task completes, the runtime analyzes what tools were used, what failed, and what strategy rotations happened. It stores these as lessons and injects them into future tasks at 5% of the context budget. So the agent actually gets better over time within a workspace.

Model routing: A complexity analyzer looks at the task and routes it — simple questions go to fast/cheap models, multi-step tasks get the expensive ones. This alone cut my API costs significantly.

Parallel tools: Up to 5 tools run concurrently. Dependency detection uses union-find grouping: read-read pairs are independent, write-write or write-read pairs get serialized, shell commands always run alone.

Agent delegation: The runtime can spawn scoped sub-agents for subtasks — each gets its own model config, tool set, and timeout. Max 10 per task, max 3 concurrent. Sub-agents can't spawn further sub-agents, so no runaway recursion.

On the infrastructure side: circuit breaker with exponential backoff and jitter for provider errors, automatic memory backend failover, a watchdog that monitors and auto-restarts subsystems, S3/R2 file storage, OpenTelemetry metrics, multi-tenant workspace isolation, and OAuth with PKCE.

The proactive trigger system (file watches, cron, webhooks, thresholds) is off by default and rate-limited. I was paranoid about an agent deciding to do things on its own, so it requires global opt-in and destructive operations need confirmation.

Everything is feature-flagged — you only compile the channels and backends you actually use. Written in Rust with tokio, 905 tests, zero clippy warnings.
Happy to answer questions about the architecture or any of the tradeoffs.
by celaleddin | 2026-03-08 12:58:09+00:00
Yes! I visited Rijksmuseum a few years ago with a close friend and I liked the painting "The Gulf of Naples with the Island of Ischia in the Distance" very much. I downloaded the high quality scan from the painting's museum web page[1] and got it printed onto a canvas to hang in our apartment. We really enjoy its presence with my wife!

[1]: https://www.rijksmuseum.nl/en/collection/object/The-Gulf-of-...
by llm_nerd | 2026-03-08 12:58:46+00:00
Here in Canada we constantly get lambasted because we're "poorer than Alabama!", at least if measured in GDP per capita is USD.

Yeah, I've been to Alabama. The absolute poorest areas of Canada are better than basically 100% of Alabama.

GDP is a myth, especially for largely illusory economies like the US where it's all a shell game of fake money, debt spending and the plutocrats that control almost all of the wealth.
by orbital-decay | 2026-03-08 13:00:29+00:00
It doesn't depend on the language at all. English, Chinese, Spanish, C++, COBOL, base64-encoded Klingon, SVGs of pelicans on bikes, emoji-ridden zoomer speak, everything is affected and has its own specific -isms and stereotypes. Besides, they're also skewed towards the pretraining set distribution, e.g. Russian generated by some models has unnatural sounding constructions learned from English which is prevailing in the dataset and where they are common, e.g. "X is/does Y, their Y is/does Z". This is likely the same for programming languages.
by xitrium | 2026-03-08 13:02:19+00:00
Can you move to a city? This is what most people I know in this situation do. Though I had a great time getting a car and taking myself out for hikes, sauna / spa days, activities and parties in the east bay near SF. Great place for practicing being alone. I had to think about it like dating myself - where would I have taken a date for fun? Try a bunch of things and see what sticks and remember you can appreciate moments by yourself with this mindset and it's like 80% as good.
by wombat-man | 2026-03-08 13:02:21+00:00
Hey, so, I live in a city but visit my parents in the suburbs once or twice a year and at it did take some work, but there are certainly third spaces. After trying a few, I found some very comfy cafes to work out of, I prefer it since my parents can be a bit distracting. Also one cafe I really like is in a 'town center' which does also have a gym. So while you may not be in a city, see if there might be any pockets of walkability you can park at and enjoy the day on your feet.
by gchamonlive | 2026-03-08 13:05:15+00:00
It's very interesting but I can't seem to find anymore tech details other than you need an older firmware, because you need to run a full chain exploit (https://www.notebookcheck.net/A-modder-has-successfully-port...)

Also, can you do this and still be able to access the original OS? It be nice to run Linux on it, but I'd also need to access my PS5 library, so do I need two machines for this?
by rokhayakebe | 2026-03-08 13:05:28+00:00
If you aren't already start working out daily and learn to make healthy meals. Not necessarily to help with loneliness, but to prevent having another problem that will only make the first worst.

If you can work from a coffee shop, if you can afford a coworking space, do it. Plus one if the new office/coffee shop is a bit far from your house.

Call your parents daily if you can.
by wombat-man | 2026-03-08 13:05:31+00:00
I've tried a number of things over the years. Sailing, climbing, running, board game meet ups, drinking meetups, golf, crossfit, curling, probably some others I'm not thinking of. Just pick something and see if it sounds interesting to you and give it a go. My big advice is to avoid shelling out on gear. Rent or just get some beginner stuff. Most of these things didn't stick, but I'm a runner and a climber and oddly I've had some great platonic connections through crossfit as well.
by benrutter | 2026-03-08 13:06:20+00:00
> I find it crazy how Python managed to get so popular when even variable name typos are a runtime error

Tangential point, but I think this might be one of the reasons python did catch on. Compile checks etc are great for production systems, but a minor downside is that they introduce friction for learners. You can have huge errors in python code, and it'll still happily try to run it, which is helpful if you're just starting out.
by adrianhon | 2026-03-08 13:06:48+00:00
Author here: Genuinely not sure how this appeared again today, but I’m glad to see it!

I have not affiliation with the team, but I really like what they do. They are currently crowdfunding €850,000 to build a permanent installation for the larp and are about a quarter of the way funded:

https://www.indiegogo.com/en/projects/ellarion-tales/odysseu...
by esskay | 2026-03-08 13:06:56+00:00
Unless you specifically need a pi (unlikely) then they really are awful value now. Hard to really go out of the way to support them now they've stuck two fingers up at the solo/indie/educational community and gone all enterprise.

Second hand mini pc's are a good option. Half the price of a pi 5 + sd + power and you often get them with 16gb ram, a decent ssd, etc.

If you need GPIO then many of the rockchip boards are still fairly affordable and easily had.
by dx111ge | 2026-03-08 13:06:57+00:00
Engram is a knowledge graph engine built as persistent memory for AI systems.
Single binary, single .brain file, no external dependencies.

I built it because LLMs have no real memory. Vector databases find similar text
but don't understand relationships or reason over facts. RAG retrieves documents
but doesn't learn from them.

Engram combines:

- Knowledge graph: typed nodes, directed edges, properties, provenance
- Hybrid search: BM25 full-text + HNSW vector + bitmap filtering
- Confidence lifecycle: facts strengthen with confirmation, weaken with time,
correct on contradiction
- Inference engine: forward/backward chaining with a rule language
- Knowledge mesh: ed25519 peer-to-peer sync between instances
- MCP server: works as a tool in Claude, Cursor, Windsurf
- HTTP REST API with 25+ endpoints and a built-in web UI

Technical details: Rust, custom mmap storage, WAL for crash recovery,
SIMD (AVX2) and GPU (wgpu) accelerated, RwLock concurrency with deferred
checkpoint. Release binary is 8.3 MB, stripped with LTO.

Free for personal/research/education use.

https://github.com/dx111ge/engram