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Recently built something where simple domain-specific heuristics crushed a fancy ML approach I assumed would win. This has me thinking about how often we reach for complex tools when simpler ones would work better. Occam's razor moments.

Anyone have similar stories? Curious about cases where knowing your domain beat throwing compute at the problem.

If I'm writing a new lightweight application that requires LLM-based text completion to power a feature, is there a standard way to request the user's operating system to provide a completion?

For instance, imagine I'm writing a small TUI that allows you to browse jsonl files, and want to create a feature to enable natural language parsing. Is there an emerging standard for an implementation agnostic, "Translate this natural query to jq {natlang-query}: response here: "?

If we don't have this yet, what would it take to get this built and broadly available?

Are you coding more or less, managing people differently, or making decisions in new ways because of AI tools? Which tools (LLMs, copilots, internal agents, analytics, etc.) have meaningfully stuck, and which turned out to be hype? I’m especially interested in concrete changes to how you plan, review work, and support teams.

Looking for recommendations for sites similar to Downdetector

I'm researching demand for an end-to-end encrypted social platform focused on private groups (family/friends) rather than public feeds.

Core thesis: "They don't own you" is becoming a movement. People want: - E2E encryption (platform can't read content) - True deletion (cryptographic, not just "marked as deleted") - Subscription model (no ads, no data mining) - Open source (verifiable claims)

The gap: Signal is for messaging, Discord isn't private, Facebook is surveillance capitalism. Nothing serves the "private social groups" use case with real privacy.

Before building, I need to validate if this is a real pain point or just something that sounds nice but nobody would actually switch for.

2-min anonymous survey: https://forms.gle/bfZYPfxMUBCc1iACA

Honest feedback appreciated—especially if the answer is "this wouldn't work because X."

Just got laid off from a chill swe job. It was remote. F. What is the quickest way to get a chill remote job where I have responsibilities and autonomy?

5YOE in JS Fullstack/ Python. SF. On student visa.

for more context, my primary language is javascript, but lately i started learning Golang,in the past i only use normal hosting providers to host my nodejs apps

Not only is 3.9 reached end-of-life, the last release was 3.9.25

% python3 Python 3.9.6 (default, Dec 2 2025, 07:27:58) [Clang 17.0.0 (clang-1700.6.3.2)] on darwin

Why are they behaving like this since last year (trying very hard to burn themselves to the ground)

Latest example:

Microsoft Office renamed to “Microsoft 365 Copilot app” (https://news.ycombinator.com/item?id=46496465)

Not only the rename is absurd, but the page (office.com) looks heavily vibe-coded

Hi HN, sorry for using a burner account.

It seems to me that up until the beginning of the last year, we saw a couple of new "open" model release announcements almost every week. They'd set a new state of the art for what an enthusiast could run on their laptop or home server.

Meta, Deepseek, Mistral, Qwen, even Google etc. were publishing new models left and right. There were new formats, quantizations, inference engines etc. and most importantly - a lot of discourse and excitement around them.

Quietly and suddenly, this changed. After the release of gpt-oss (August 2025), the discourse has been heavily dominated around hosted models now. I don't think I've seen any mention of Ollama in any discussion that reached HN's front page in the last 6 months.

What gives? Is this a proxy signal that we've hit a barrier in LLM efficiency?

First off, this thread is NOT a petition to rally against the moderation team. Considering the deluge of trash they deal with every day, I think they are doing a valiant job and are to be commended. Consider it merely a place to discuss, which is what HN does best.

That said, it's becoming more and more obvious every day that there is a tremendous amount of attempts by bots, and specifically AI agents, to inject slop into HN threads. I worry about the integrity of the discourse here and if the ever growing wave of garbage will overtake staff resources to deal with it. Is it time to implement captcha for HN? If so, should it be out of the box, or a new mechanism more tailored to the security and privacy-centric nature of the HN readership? Are captchas even still effective enough in the age of AI to warrant their use?

The best phone in 2026 is a dying iPhone 12 mini. It’s the smallest smartphone still maintained by a major manufacturer (both software updates and repair parts), runs all apps securely & as designed, costs ~$150 with accessories available for dirt cheap (my favorite sleeve officially made by Apple [1] cost $129 on release but is now available for around $10). A dying battery makes the phone better - it forces you use the phone less and only when you really need it. Don’t replace your battery, just turn the phone off and you’ll feel immediately less stressed. If you don’t want to miss calls, get an old Apple Watch with cellular and use it as your main “phone”. Buy a dying iPhone mini to save the planet and yourself, and maybe if Apple sees enough active minis they will make another one...

[1]: https://apple.fandom.com/wiki/IPhone_12_Leather_Sleeve_with_MagSafe

Finding a true “Remote from Anywhere” role is harder than it looks.

Many jobs are labeled “remote,” but the fine print often ties them to a region, a time zone, or specific legal and tax requirements.

Here are practical checks that help you spot “remote anywhere” roles faster, and avoid common red flags.

1) Read the location line Start with the simplest signal: is there a geography attached?

- “US Remote” “Remote (EU)” “LATAM only” or “Remote within X countries” usually means location restrictions. - If time zones are listed, that can also imply location limits, even when the role is technically remote. - Look for explicit language like “Global remote,” “Work from anywhere,” “fully asynchronous,” or “distributed team across multiple countries.” These are not guarantees, but they are stronger indicators.

2) Treat salary as a clue

Pay ranges can indicate the target hiring market.

- A range like $100k to $250k often signals a US-centered market (not always, but often).

3) Watch the application form

Sometimes the job post is vague, but the ATS form tells the truth:

- Questions like “Which time zone can you work in?” can reveal the required overlap. - If the location dropdown includes only a few regions (e.g., US, Canada, Europe, Other), it often indicates there are specific geographic requirements. - Red flags that usually indicate US-only hiring include questions about US work authorization, a US tax ID, US-specific benefits or requirements such as Security Clearance.

4) Check the company on LinkedIn

If a company truly hires globally, you can usually see it in its team.

- Review employee locations. Even if LinkedIn shows only a few “top locations,” individual profiles reveal the real spread. - Search for your profession (e.g., Software Engineer) and check where they actually live. - If you see people working from India, Asia, Africa or other regions beyond the US and Europe, that is a strong sign the company can hire internationally.

5) Compare career pages and external job boards

Job descriptions are sometimes more detailed on the company website.

- Look for mentions of an asynchronous culture, a multi-national team, or the number of nationalities in the company. - Check LinkedIn job posts and external job boards. They sometimes include location constraints that are missing from the official posting.

Remote anywhere roles exist, but they are a narrower category than most people expect.

Companies balance time zone collaboration, employment compliance, payroll, and security requirements.

Good luck with your remote job search!

## My Implementation

I'm using a 64-bit layout:

- Bits 63-51: Quiet NaN signature (0x7FFC...)

- Bits 50-18: 32-bit payload (integers, string pool indices, etc.)

- Bits 17-3: Unused/ (15 bits)

- Bits 2-0: 3-bit type tag

So it allows me to have 5 tagged types: `TRUE_VAL`, `FALSE_VAL`, `STRING_VAL`, `CALLDATA_VAL`, `U32_VAL`

This is for a domain-specific VM I'm building for programmable task management (think "Vim for todo apps" - small core with scriptable behaviors). The VM is stack-based with:

- String pooling & instructions pooling (indices stored as NaN-boxed values) - Call stack for task instructions execution.

code is here : https://github.com/tracyspacy/spacydo/blob/main/src/values.rs

As the title implies I'm experiencing a lull.

I'm a software engineer in games and big tech for a combined 15 years. Coding is my biggest passion and I'll still do it when I retire.

However, a large part of me wants to get into an entirely different industry.

I've identified the reason to be how intellectually unstimulating coding has become (due to vibe coding).

The AI tools are incredible - but in terms of learning and self development, vibe coding as an education form lacks depth and the classic sense of mastery. I feel like I've learned 90% of everything there is to know about vibe coding. The last 10% is just marginal gains.

I live for the thrilling moments in software development - I was in the industry early enough to roll out software on our own server racks. As a game dev, I regularly implemented algorithms (such as path finding, physics integrations and rendering logic). I also got to work with Mixed Reality headsets and help define UX paradigms.

I feel like there's now a stigma around DIY coding - one should simply use an off-the-shelf solution or AI.

I get so much pleasure in doing deep work, but nowadays, any feature that takes more than 2 days to implement gets eyebrows raised.

Am I overthinking it?

I’m part of the Developer Crew at io.net, where we’re working on decentralized compute for AI workloads and agent-based systems.

My focus here isn’t promotion, but sharing practical learnings from the builder side things like how AI agents behave when compute is modular, what breaks in real usage, and how developers actually think about decentralized infra in production.

I’m here to learn from the community, contribute where I can, and exchange notes with others building or experimenting in this space.

Happy to answer questions or dig into specifics if useful.

Dear Americans, please don’t take this the wrong way - I love the US, have friends there, and treasure memories I made there.

However, it seems plausible that the US is turning into a rogue, authoritarian, Russia-like state increasingly more friendly towards Russia and hostile towards Europe. I am a European who grew up in a country still occupied by Russia. I am increasingly more worried about building my projects on American platforms, using an American operating system, etc.

What if the US actually attacks Greenland or finds another way to be openly hostile to Europe? I am not saying it will happen. All I am saying is that it seems prudent to prepare. How would you do it?

It is currently impossible to unhook myself from the US, but I would like to minimize exposure.

I can’t do anything about things like building an alternative to VISA/MasterCard (except wait for the digital Euro), so I will focus on things I can actually do and ignore things like my government buying F-35s and possibly giving my health data to Palantir.

* Mobile phone - there are no real European alternatives; it’s just Apple vs Google. Samsung or HTC with Android seems like a less bad option.

* Operating system - I have been using Linux for ages, getting rid of Windows seems relatively easy.

* Social networks - I grew to hate them before the current US admin, never used TikTok or Instagram, and I mostly stopped using Facebook and Twitter around the time Musk bought Twitter.

* Stripe for payments - this will be hard, but I am experimenting with our local payment processor, and so far it seems surprisingly doable, but it is not a battle-tested solution like Stripe.

* Clerk authentication - doable, but a lot of work and worrying

* AWS - I had a surprisingly bad experience with AWS and switched to a local provider with a lot less functionality (that I mostly do not need) and a lot better support

* GitHub, Cloudflare... dear God, how could we Europeans allow ourselves to be that dependent on anyone? Everything I touch is American.

* Gmail - this will be hard (two decades of emails). Any advice?

* Anything AI-related - fuuu, I am lost here.

What am I missing/forgetting? What do/would you do in my place?

I really hope you will take this as a brainstorming exercise and not an attack on America. I really do love the US and hope its democracy turns out to be more resilient than it currently seems.

EDIT: Please kindly keep responses practical. Let’s not turn this into a political discussion. You might approach it as a “what if” exercise, even if you think what the current US admin is doing is great, Europeans deserve what they get, etc.

i'm interested to know more about the international payment system but most of the data online is looking more and more biased so I'm here to do recon

i'd love to know what's your preferred payment method for digital payments and why, any story you share would be great too

ps: please start with your country like

"US: <response>", it'll help me categorise better

thank u, in advance

I’m researching patterns for edge / gateway telemetry where the network is unreliable (remote sites, industrial, fleets, etc.) and you need offline buffering + bounded disk + replay once connectivity returns.

Questions for folks running this in production:

What do you use today? (MQTT broker + ??, Kafka/Redpanda/NATS, Redis Streams, custom log files, embedded DB, etc.)

Where do you buffer during outages: append-only log, SQLite/RocksDB, queue-on-disk, something else?

How do you handle backpressure when disk is near full? (drop policy, compression, sampling, prioritization)

What’s your failure nightmare: corruption, replay storms, duplicates, “stuck” consumer offsets, disk-full, clock skew?

What guarantees do you actually need: zero-loss vs “best effort” (and where do you draw that line)?

What metrics/alerts matter most on gateways? (queue depth, replay rate, oldest event age, fsync latency, disk usage, etc.)

I’d love to learn what works, what breaks, and what you wish existing tools did better.

I have a decade of mixed experience: 10+ years in IT, 2 years on a professional motorsports team (Tony Kart, Ducati, Miata), and 4 years in software (refactoring old .NET to Spring Boot/Angular and some greenfield projects). I self-taught my way into dev and finished a CS degree while working.

I’m currently at a {big_slow_corp} and feeling the AI squeeze trying to switch jobs. My exp is not public facing so the “experience building scalable apps to disrupt the market” requirement I think is my weakness. I’ve also realized my heart isn’t in web frameworks. I’m the guy bored on a plane talking to his gf about flap software, throttle response, and suspension geometry like a kid showing a toy to his mom.

I want to move "closer to the metal", ECUs, controlling machinery, I’d love ML applied to machinery, even infotainments!, but my professional resume is strictly high-level. My tinkering experience includes building MegaSquirts and using ESP32s for signal filtering and logic, data loggers and wiring things you’d find in a race car, but I currently lack a project car to demonstrate new work.

How can I bridge the gap from Java/Angular to automotive software?

Does a decade of IT and 4 in backend experience carry weight in the "Software Defined Vehicle" world, or am I starting at zero?

What "proof of work" can I build to wow recruiters at places like Toyota Research or Tesla (any brand with US presence really) without a physical car to hack on?

I'm ready to pivot to where my passion actually is. Any advice is appreciated.

Everyone’s hyped about Claude Code, Nano Banana models, and how AI can now “build a game in 30 minutes for $ 20.”

If that’s true—why don’t we see a flood of ultra-low-cost, high-quality tower defense games (like Kingdom Rush) built with AI, raking in serious revenue?

After all:

Art? → Image generators + style locking. Code? → LLMs can scaffold Unity/Godot logic. Balancing? → Maybe RL or even manual tuning on a tiny scope. Audio? → AI voice + music tools exist.

And yet… the App Store isn’t overrun with polished, profitable Kingdom Rush–style games made by solo devs using AI. Why?

P.S. Yes, this post was AI-generated—my first attempt at market research before launching Kingdom Rush: Nano Banana Edition.

If you or your organization are developing projects that advance amateur radio or digital communications, now’s a great time to apply for ARDC grant funding, with our next application deadline on February 1, 2026.

ARDC’s priority areas for funding reflect our broader vision and strategy for supporting experimentation, education, and open technology within the amateur radio and digital communications communities. We’re especially interested in projects that align with these three areas:

Research & Development (R&D): open hardware and software systems that enable learning and experimentation (e.g. SDRs, open codec technologies, new modulation techniques).

Space-Based Communications: projects that create or expand access to satellite communications for amateur radio (AR) and digital communications (DC), engaging communities in wireless experimentation (e.g. GEO or HEO programs, repurposed commercial satellites, space-based tools for learning).

Open Source Education: scalable, open educational materials and hands-on projects that make AR and DC more accessible, especially for new learners and clubs (e.g. curricula, videos).

While we welcome proposals across the full range of AR and DC, projects that align with these areas remain a priority in our grantmaking decisions.

Learn more about these priority areas at https://www.ardc.net/apply/priority-areas-for-funding/, and find information on eligibility and how to apply at https://www.ardc.net/apply/. For additional questions, contact giving@ardc.net.

I started building Super Productivity in late 2016 because I needed to log time against my Jira tickets. Ironically, I've never had to do that again on any project since.

But I kept building it anyway and for some reason I couldn't stop doing it. 8 years later it's a local-first task manager with time tracking and integrations for Jira, GitHub, GitLab, and others. Everything runs on your device - no cloud, no account required.

Why local-first? Three reasons: - I didn't want to run servers or deal with auth systems - I care about not having my work habits tracked - I needed something that works offline - Most of the companies I worked for would not allow for putting that kind of data into a random cloud service

Biggest lesson from 8 years: saying no is sometimes harder than building features. Every "quick addition" someone requests has hidden complexity and long term costs. I know how much effort goes into drafting ideas, so I often had a very hard time saying no to new additions, especially if people already provided the code and even more if it was good clean and tested code.

Now there is a plugin system with community plugins and this makes it much easier.

Still figuring out: sustainable funding without ads or selling data. Currently, it's donations + my own time. Would love to hear how others approach this.

Repo: https://github.com/johannesjo/super-productivity Try it: https://super-productivity.com

All you need to do is to call it with @rho and tell it which environment you want to deploy to. GCP cloud run is currently supported.

I've seen a few anecdotes lately that say that they use Claude Code on legacy codebase and with relatively little supervision it can work on complex problems. Then the claim that Claude Code writes most of its own code, and that they no longer mentor their newcomers - instead, AI answers their questions and they can start making meaningful changes within the first few days. To me it sounds almost too good to be true, so I'd love to have some reality check.

I've spent most of my career in legacy codebases, reading, tracing behavior, making careful changes, and writing tests to protect them. I've taken a sabbatical though, which ends soon, and I'm quite worried and excited to what has happened during this time.

For those working on legacy codebases:

- Has the workflow really shifted to prompting AI, reviewing output, and maintaining .md instructions?

- Does your company allow Claude Code, Codex or similar tools? If not, what do you use?

- Do companies worry about costs and code privacy?

- Where do you think this is headed, a year from now?

Concrete examples, good or bad, would be especially helpful. Thanks.

Frasier is one of my favorite shows of all time. I love the characters, the writing, the acting, the whole kit and kaboodle.

I've been watching it on Prime and it's been driving me insane how drivel and nonsensical their season and episode AI overviews are.

Here is the overview for season eight:

  In the eighth season of Frasier we meet Daphne and Niles post elopement and dealing with Donny suing Daphne. Meanwhile, Frasier reluctantly becomes 'mentor' to station KACL's young new owner, Silicon Valley billionaire Kenny Daly

What is going on? Amazon's a billion fucking dollar company. Just pay for IMBD's overviews. Oh, that's right, Amazon owns IMDB because of course they do.

I am so sick and tired of this AI slop infecting everything.

I built Gitmore. Connects via webhooks, tracks commits and PRs, and gives you one dashboard. AI answers questions about activity, sends weekly reports to Slack/email. Connect agent to slack which answers your questions directly from your workspace. https://gitmore.io (free for 1 repo).

Doing research is extremely frustrating these days. I feel like either I'm going nuts or search has been nerfed.

My search queries are frequently misinterpreted. Google, bing, duckduckgo have a strong recency bias and don't surface older webpages.

These problems get worse for niche and technical topics. I can't even find existing webpages that I know I've found / read about in the past.

What's going on? Why is the modern search experience so frustrating?

Hi everyone I saw zig is and intresting language I am learning it and also making a transpilied High level language over it I want some help with developing syntax. in my lang there are three types of var declaration 1) using local keyword this are added in arena of the specific function. 2) using let keyword this are on stack but i am finding solution to make strings easier here. 3) manual memory but my transpiler will automatically use defer keywords so they are safe and delete once block exit 4) using unsafe direct fully manual memory management but still my transpiler will not let compiler till once in code the are freed.