MurphyA/hacker-news huggingface dataset:
by pavlov | 2026-02-08 08:54:57+00:00
This happened to painters and illustrators a few years earlier than programmers.

People used to think detailed drawing and realistic painting were kinds of unachievable wizardry. Now they go on ChatGPT or Midjourney and press a button. Professionals despair at the slop results, but they’re good enough for most people.

I try to think of this positively as a transition similar to the invention of photography. The bourgeois classes used to need artists to paint pictures of themselves and their daughters. The camera turned portrait creation into a one-button affair. But that actually freed artists to be more creative. Monet and Picasso happened after photography for an obvious reason.
by sizzleflip5000 | 2026-02-08 08:57:21+00:00
Americans know their leadership isn't popular everywhere, especially when leadership of other governments disagrees with them on increasingly more issues (UK and French censorship being one of them, ironically).

Seeing the 100th "U.S. government bad - please believe us this time" story from yet another activist-masquerading-as-journalist from The Guardian (UK) ending up on a website for tech stuff is the real travesty here.
by sublinear | 2026-02-08 08:57:28+00:00
Your example actually perfectly describes why it won't displace anyone.

The user still has to be in the loop to instruct the LLM, and the tiniest nuances in that work still matter. If it didn't we'd have replaced people with bash scripts many decades ago.

What this actually does is make people more available for meeting time. Productivity doesn't budge at all. :)

In other words, the "busy work" has always been faster to do than the decision making, and if someone has meetings to attend they don't strictly do busy work.
by azamsayeedit | 2026-02-08 08:58:15+00:00
y community,
I'm working on Daily Trending (daily-trending.org) — powered by Google Trends + social buzz + headlines + all the latest trending and viral things happening around you. It surfaces what's spiking right now (viral memes, breaking news, cultural moments, search surges) before they peak, so you can create timely content fast. No outdated lists or guesswork — just instant insights via a clean dashboard. Great for:Journalists chasing the next story Marketers spotting campaign opportunities

Creators staying relevant on social/newsletters/videos

Thousands already use it to get ahead. It's free to use, AI-curated & updates continuously.Quick pitch: If you're building/launched something recently, how do you stay on top of trends without wasting time? What frustrates you most about current tools (Google Trends UI, delayed data, etc.)?

Would love your honest feedback:What features would make this 10x more useful for makers?

Any pain points with trend-spotting you'd want solved?

Check it out here: https://www.daily-trending.org Excited to hear your thoughts — let's chat!

Follow-up replies (you can add as thread replies later):Share a specific trend example you caught early

"What's trending in your niche right now?"

https://www.daily-trending.org
by az226 | 2026-02-08 08:59:05+00:00
It is not crazy.

Each engineer is very valuable. LLM tokens are cheap. You scale up inference compute, and your engineers can focus on higher order stuff, not reviewing incorrect responses, validating bugs, and what not.

It’s shocking to me that there isn’t a $2,000 / $20,000 per month subscription tier for coding assistants. I’ve always in my mind called this ExecGPT since around 2021, but the notion was that executives have teams that support them to be high functioning and high leverage, responsible for quality of thinking and decision making, not quantity of work output.

And the value/prop existed and continues to exist even as the models get smarter, even Opus 4.6.
Show HN: ShipClaw – Deploy OpenClaw to the Cloud in One Click
by sunpy | 2026-02-08 09:00:09+00:00
I built ShipClaw (https://shipclaw.app) to solve a problem I kept running into: deploying OpenClaw that need to run 24/7 is surprisingly painful.

If you've tried running OpenClaw, you know the drill – spin up a VPS, install dependencies, configure environment variables, set up monitoring, handle crashes... all before the agent even starts doing useful work.

ShipClaw gives you managed infrastructure specifically for AI agents:

- One-click deployment from a dashboard
- All major AI models (Claude, GPT, etc.) preconfigured
- Browser automation & web search built in
- 2-8 GB RAM, 20-100 GB SSD depending on plan
- Real-time logs and monitoring
by tsoukase | 2026-02-08 09:00:41+00:00
About 20 years ago I realised the extreme immaturity of IT field. The market was fragmented and you could literally become billionaire from your basement (80's), from an office (90's) etc. It was at the stage of car industry of the beginning of the past century. Since then the IT market has matured a lot so that a handful of companies formed an oligopoly and are thriving with no possibility of threat from isolated developers. In the following years the startup landscape will be dead, everyone will be bought up and even the computer devices will become sealed boxes with controlled buttons. Having a browser open might be a hack.
by sothatsit | 2026-02-08 09:01:19+00:00
Dylan Patel did analysis that suggests lower batch size and more speculative decoding leads to 2.5x more per-user throughput for 6x the cost for open models [0]. Seems plausible this could be what they are doing. We probably won't get to know for sure any time soon.

Regardless, they don't need to be using new hardware to get speedups like this. It's possible you just hit A/B testing and not newer hardware. I'd be surprised if they were using their latest hardware for inference.

[0] https://nitter.net/dylan522p/status/2020302299827171430
by AlexeyBelov | 2026-02-08 09:02:10+00:00
I think their point was: at this point we should see clear macro effects. There should be new software that wasn't possible yesterday (or feasible, etc). Something big and new, and high quality.

AI helps me too, and I feel like I ship more now. But what if it's just a feeling and I'm not doing anything groundbreaking? What if I'm just doing what I've always done but at slighly more speed?
Show HN: AgentLens – Open-source observability and audit trail for AI agents
by amit_paz | 2026-02-08 09:02:40+00:00
Built AgentLens to solve a problem we kept hitting: once you deploy AI agents, you have no idea what they're actually doing. No traces, no costs, no audit trail.

AgentLens is an open-source observability platform for AI agents. It captures every tool call, LLM interaction, and decision as tamper-evident events with hash-chain integrity.

Key features:
- MCP-native integration (Model Context Protocol)
- Tamper-evident audit trail with SHA-256 hash chains
- Cost tracking across models and sessions
- Real-time SSE streaming dashboard
- Alerting engine (error rate, latency, cost thresholds)
- TypeScript SDK for custom integrations

Stack: TypeScript, Hono, React, Drizzle ORM, SQLite, MCP SDK.

5 npm packages under @agentlensai (core, server, mcp, sdk, cli). 413 tests. MIT licensed.

Part of a broader ecosystem with AgentGate (human-in-the-loop approvals) and FormBridge (structured data collection for agents).

Would love feedback on the architecture and what observability features matter most to you.
by anonnon | 2026-02-08 09:03:23+00:00
> After working with the latest models I think these "it's just another tool" or "another layer of abstraction" or "I'm just building at a different level" kind of arguments are wishful thinking. You're not going to be a designer writing blueprints for a series of workers to execute on, you're barely going to be a product manager translating business requirements into a technical specification before AI closes that gap as well

I think it's doubtful you'll be even that; certainly not with the salary and status that normally entails.

> I'm very convinced non-technical people will be able to use these tools

This suggests that the skill ceiling of "Vibe Coding" is actually quite low, calling into question the sense of urgency with which certain AI influnecers present it, as if it were a skill that you need to invest major time & effort to hone now (with their help, of course), lest you get left behind and have to "catch up" later. Yet one could easily see it being akin to Googling, which was also a skill (when Google was usable), one that did indeed increase your efficiency and employable, but with a low ceiling, such that "Googler" was never a job by itself, the way some suggest "prompt engineer" will be. The Google analogy is apt, in that you're typing keywords into a blackbox until it spits out what you want; quite akin to how people describe "prompt engineering."

Also the Vibe Coding skillset--a bag of tricks and book of incantations you're told can cajole the model--has a high churn rate. Once, narrow context windows meant restarting a session de novo was advisable if you hit a roadblock, but now it's usually the opposite.

If this all true, then wouldn't the correct takeaway, rather than embracing and mastering "Vibe Coding" (as influencers suggest), be to "pivot" to a new career, like welding?

> The irony is that I haven't seen AI have nearly as large of an impact anywhere else. We truly have automated ourselves out of work, people are just catching up with that fact

What's funny is artists immediately, correctly perceived the threat of AI. You didn't see cope about it being "just another tool, like Photoshop."
by TwoNineFive | 2026-02-08 09:03:28+00:00
COVID saved Trump. His policies, such as the section 174 tax change, were tanking the economy in 2019, and we would have likely seen a recession in 2020-2021 instead of 2022.

His solution to COVID was to tell the public to inject themselves with bleach. Or just go get infected and let natural selection figure it out.

It was no more a joke than the racist cartoon he posted the other day.

You need consequences.
by pjmlp | 2026-02-08 09:04:09+00:00
I learnt C++ on Turbo C++ 1.0 for MS-DOS as teenager, after doing C with Turbo C 2.0.

Being on Borland ecosystem that was followed by several years of Object Pascal (I was already using TP at the time), and C++.

Never got the point of why to keep using C, other than external constraints like school assignments, jobs requirements or lack of compilers.
by bdd | 2026-02-08 09:04:10+00:00
Works on this Postgres 17.7:

postgres=# show server_version;
server_version
-------------------------------
17.7 (Debian 17.7-3.pgdg13+1)
(1 row)

postgres=# SELECT
sum(blks_hit)/nullif(sum(blks_hit+blks_read),0) AS cache_hit_ratio
FROM
Pg_stat_database;
cache_hit_ratio
------------------------
0.99448341937558994728
(1 row)
by burnt-resistor | 2026-02-08 09:04:45+00:00
5.25" disks include a synchronization index hole read by the drive and 3.25" disks align using a hub mechanism. These wouldn't be too difficult to align factory-duplicated floppies in a custom jig that includes a laser or similar small, focused heating element.

The risk of this style of approach is that it must be compatible with every single drive manufactured combined with every floppy controller because it must produce exactly the same OS-level error pathology with intentional physical damage. (Another approach is inducing low level MFM errors without physically modifying the media but it requires special hardware.)

Also, as with physical hasps (dongles), copy protection magic codes, install floppy disk writable decrementing counters, damaged sector key disks, and pretty much every other technique, these checks always exist somewhere as binary instructions in the executable and can be located with a debugger and/or hex editor through binary search and/or call stack tracing heuristics in a relatively short time.
by sshine | 2026-02-08 09:04:59+00:00
> LARP'ing CEO

My experience with plain Claude Code is that I can step back and get an overview of what I'm doing, since I tend to hyperfocus on problems, preventing me from having a simultaneous overview.

It does feel like being a project manager (a role I've partially filled before) having your agency in autopilot, which is still more control than having team members do their thing.

So while it may feel very empowering to be the CEO of your own computer, the question is if it has any CEO-like effect on your work.

Taking it back to Claude Code and feeling like a manager, it certainly does have a real effect for me.

I won't dispute that running a bunch of agents in sync won't give you an extension of that effect.

The real test is: Do you invoice accordingly?
by arjie | 2026-02-08 09:05:00+00:00
The return of the Web of Trust, I suppose. Interesting that if you look at the way Linux is developed (people have trees that they try to get into the inner circle maintainers who then submit their stuff to Linus's tree) vs. this, it's sort of like path compression in a union-find data structure. Rather than validating a specific piece of code, you validate the person themselves.

Another thing that is amusing is that Sam Altman invented this whole human validation device (Worldcoin) but it can't actually serve a useful purpose here because it's not enough to say you are who you are. You need someone to say you're a worthwhile person to listen to.
by ares623 | 2026-02-08 09:05:18+00:00
At first I was partially impressed by the Digital Twin Universe thing they describe. Having worked with 3rd party APIs in a previous life, having something like that would've been so much help.

But after thinking about it more, I think it must be the lowest of low hanging fruits for LLMs. You're building something with well defined specs, most of which is readily available by the original creators, with a UI that only does the bare minimum, and it doesn't need any long-term features like reliability since it's all for internal short-lived use. On top of that, it looks super impressive when used in a demo, because all those applications being mocked are very complicated pieces of software. So to recreate a thin facade of them can look very impressive. And calling it a "Digital Twin Universe" is just icing on the cake.
by hunvreus | 2026-02-08 09:05:23+00:00
I remember reading The Twelve-Factor App [1] from the Heroku folks back in the day, and was blown away by how well they understood the problem. Not only that but they had great taste.

I moved things to Render a while back, and then to my own Hetzner server (I built kind of an open source Vercel clone for that reason [2]).

I'm not quite sure any of these platforms are going to be relevant 5 years from now when you can summon your own DevOps AI agent. At the very least it's going to be incredibly difficult to justify the premium on top of AWS.

1: https://www.12factor.net/

2: https://devpu.sh
by fleabitdev | 2026-02-08 09:05:53+00:00
You've rediscovered a state-of-the-art technique, currently used by JPEG XL, AV1, and the HEVC range extensions. It's called "chroma from luma" or "cross-component prediction".

This technique has a weakness: the most interesting and high-entropy data shared between the luma and chroma planes is their edge geometry. To suppress block artefacts near edges, you need to code an approximation of the edge contours. This is the purpose of your quadtree structure.

In a codec which compresses both luma and chroma, you can re-use the luma quadtree as a chroma quadtree, but the quadtree itself is not the main cost here. For each block touched by a particular edge, you're redundantly coding that edge's chroma slope value, `(right_chroma - left_chroma) / (right_luma - left_luma)`. Small blocks can tolerate a lower-precision slope, but it's a general rule that coding many imprecise values is more expensive than coding a few precise values, so this strategy costs a lot of bits.

JPEG XL compensates for this problem by representing the local chroma-from-luma slope as a very-low-resolution 2D image (one "pixel" per 64x64 block), and then recursively compressing that data as a lossless JPEG XL image. This is similar to your idea of using PNG-like compression (delta prediction, followed by DEFLATE).

Of course, since you're capable of rediscovering the state of the art, you're also capable of improving on it :-)

One idea would be to write a function which, given a block of luma pixels, can detect when the block contains two discrete luma shades (e.g. "30% of these pixels have a luminance value close to 0.8, 65% have a luminance value close to 0.5, and the remaining 5% seem to be anti-aliased edge pixels"). If you run an identical shade-detection algorithm in both the encoder and decoder, you can then code chroma information separately for each side of the edge. Because this would reduce edge artefacts, it might enable you to make your quadtree leaf nodes much larger, reducing your overall data rate.
by red75prime | 2026-02-08 09:09:18+00:00
Randomness is not a problem by itself. Algorithms in BQP are probabilistic too. Different prompts might have different probabilities of successful generation, so refinement could be possible even for stochastic generation.

And provably correct one-shot program synthesis based on an unrestricted natural language prompt is obviously an oxymoron. So, it's not like we are clearly missing the target here.
by Sharlin | 2026-02-08 09:09:52+00:00
Unlike in image/video gen, at least with LLMs the "best" solution available isn’t a graph/node-based interface with an ecosystem of hundreds of hacky undocumented custom nodes that break every few days and way too complex workflows made up of a spaghetti of two dozen nodes with numerous parameters each, half of which have no discernible effect on output quality and tweaking the rest is entirely trial and error.
by manuelmoreale | 2026-02-08 09:11:10+00:00
Call me weird, I know absolutely nothing about what you just wrote but I'm super intrigued now and would love to know more, ah. But that's just because I'm generally curious about pretty much everything.

But I can see why it can be hard to find people to talk about that. Heck it might be hard to find people who even know about that topic in general.
by okintheory | 2026-02-08 09:11:24+00:00
I think you're misunderstanding the point this paper is trying to make. They're interested in trying to distinguish whether AI is capable of solving new math problems or only capable of identifying existing solutions in the literature. Distinguishing these two is difficult, because self-contained math problems that are easy enough for LLMs to address (e.g. minor Erdos-problems) may have been solved already as subcomponents of other work, without this widely known. So when an AI makes progress on such an Erdos problem, we don't know if it had a new idea, or correctly identified an existing but obscure answer. This issue has been dogging the claims of AI solving Erdos problems.

Instead, here you get questions that extremely famous mathematicians (Hairer, Spielman) are telling you (a) are solvable in <5 pages (b) do not have known solutions in the literature. This means that solutions from AI to these problems would perhaps give a clearer signal on what AI is doing, when it works on research math.
by skeptic_ai | 2026-02-08 09:12:11+00:00
Think Epstein but in code. Everyone would vouch for him as he’s hyper connected. So he’d get a free pass all the way. Until all blows in our faces and all that vouched for him now gets flagged. The main issue is that can take 10-20 years for it to blow up.

Then you have introverts that can be good but have no connections and won’t be able to get in.

So you’re kind of selecting for connected and good people.
by ahartmetz | 2026-02-08 09:12:54+00:00
AFAIU it is possible to get nearly full resolution and full frame rate by using motion tracking to merge the fields. I.e. if the motion is regular enough, lines from the previous and / or next frame can be inserted instead of doubling the current lines. Which is almost the same process required to achieve best image quality at half the frame rate, just without throwing away frames.
by itmitica | 2026-02-08 09:13:13+00:00
În real world, worst performers get thrown out of the loop if they are flagged and fail to improve. Assumption here seems to be agents have no such issues. Am I missing something?

In the real world, over engineering is the play to work-pretend sped money, with more theatre than results. Am I missing something here too? Deep agentic factories? Deepest agentic factories?
by wundersam | 2026-02-08 09:13:37+00:00
Appreciate the transparency about the AI-assisted development. Your concerns about code quality are valid, but you're overthinking it. We've all shipped worse code that we wrote ourselves.The real win here is that you listened to feedback and made it verifiable. That's what the privacy-conscious Android community needed. The fact that it already works well in production is a bonus.
by ChrisArchitect | 2026-02-08 09:14:20+00:00
It isn't about the tools or using them, it's about the scale. The scale of impact is immense and we're not ready to handle it in a mutitude of areas because of all the areas technology touches. Millions of jobs erased with no clear replacement? Value of creative work diminshed leading to more opportunities erased? Scale of 'bad' actors abusing the tools and impacting a whole bunch of spheres from information dispersal to creative industries etc. Not even getting into environmental and land-use impacts to spaces with data centers and towns etc (again, it's the scale that gets ya). And for what? Removing a huge chunk of human activity & expression, for what?
by MobiusHorizons | 2026-02-08 09:14:46+00:00
The “fancy jump” is the branch instruction. As far as I know all ISAs have them. Even rv32i which is famously minimal has several branch instructions in addition to two forms of unconditional jump. Branches are typically used to construct if / for / while as well as && and || (because of short circuiting) and ternary (although some architectures may have special instructions for that that may or may not be faster than branches depending on the exact model). Without it you would have to use computed goto with a destination address computed without conditional execution using constant time techniques.
by cstdr | 2026-02-08 09:15:27+00:00
Have you actually tried it? Because I did some months ago to setup an AppleTV and it just does not work. It just hangs at the last step without telling you anything. If you inspect the server response it just says "Your account cannot be created at this time.".

What ultimately helped after weeks of trying and tinkering was installing VMware Workstation, patching it to enable macOS support, create a VM with the specific hardware configuration of an older MacBook, install an old version of macOS and do the 2FA from in there.
by nudpiedo | 2026-02-08 09:15:52+00:00
I think the only people who burnt of the discussion were people who is terminally online. But in the industry, there is people in any paradigm with any previous development times you can remember of.

> Is there any formal language that is usable as general-purpose programming language I don't know of?

That’s sort of my point, the closest thing to a rich type system yet pragmatic enough is to me F# and it’s still falls short as formal verification and ecosystem integration.

I think eventually, we should invest into this direction so LLM production can be trusted, or even ideally, producing or helping with the specific specifications of models. This is yet to be done.

I don’t want to make a prophecy, but the day, ergonomics and verification meet in an LLM automated framework, this new development environment should take over everything previous.
by avoutic | 2026-02-08 09:16:17+00:00
Hitting production APIs (and email) is my main concern with all agents I run.

To solve this I've built Wardgate [1], which removes the need for agents to see any credentials and has access control on a per API endpoints basis. So you can say: yes you can read all Todoist tasks but you can't delete tasks or see tasks with "secure" in them, or see emails outside Inbox or with OTP codes, or whatever.

Interested in any comments / suggestions.

[1] https://github.com/wardgate/wardgate