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Hello there!

I just set up TinyTinyRSS (https://tt-rss.org/) at home and I'm looking into interesting things to read as well as people/website publishing interesting stuff.

This, among the other things, to reduce the daily (doom)scrolling and avoid the recommendation algorithms by social media.

So: who or what do you follow via RSS feed, and why?

I’ve always thought that DDD was a surprisingly good debugger for its time.

It made program execution feel visible: stacks, data, and control flow were all there at once. You could really “see” what the program was doing.

At the same time, it’s clearly a product of a different era:

– single-process

– mostly synchronous code

– no real notion of concurrency or async

– dated UI and interaction model

Today we debug very different systems: multithreaded code, async runtimes, long-running services, distributed components.

Yet most debuggers still feel conceptually close to GDB + stepping, just wrapped in a nicer UI.

I’m curious how others think about this:

– what ideas from DDD (or similar old tools) are still valuable?

– what would a “modern DDD” need to handle today’s software?

– do you think interactive debugging is still the right abstraction at all?

I’m asking mostly from a design perspective — I’ve been experimenting with some debugger ideas myself, but I’m much more interested in hearing how experienced engineers see this problem today.

Lately i've been experimenting with this template in Claude's default prompt ``` When I ask a question, give me at least two plausible but contrasting perspectives, even if one seems dominant. Make me aware of assumptions behind each. ```

I find it annoying coz A) it compromises brevity B) sometimes the plausible answers are so good, it forces me to think

What have you tried so far?

Almost all transactional emails are being marked as suspicious even when their SPF/DKIM records are fine and they’ve been whitelisted before. Did Google break something in gmail/spam filtering?

After launching my first few apps, I'm running into an unexpected problem: younger children are the ones most likely to click my ads, using their parents' phones/tablets. This gets around the age restriction, and in fact, has a compounding effect: they are too young to have their OWN devices (so they just install and never use again), burn ad money, and most importantly, skew the target demographic towards users like them- so ad placements end up targeting more of them, because they're most likely to click!

This causes all my campaigns to immediately fail, no matter how much I play with the age restriction (again, parent's device). I've changed settings to stop showing in games and on tablets, which has helped a bit, but not fully.

Obviously, there is no chance I'm adding real age verification to the app itself.

The first time I pulled usage costs after running Chatter.Plus - a tool I'm building that aggregates community feedback from Discord/GitHub/forums - for a day hours, I saw $2.30. Did the math. $70/month. $840/year. For one instance. Felt sick.

I'd done napkin math beforehand, so I knew it was probably a bug, but still. Turns out it was only partially a bug. The rest was me needing to rethink how I built this thing. Spent the next couple days ripping it apart. Making tweaks, testing with live data, checking results, trying again. What I found was I was sending API requests too often and not optimizing what I was sending and receiving.

Here's what moved the needle, roughly big to small (besides that bug that was costin me a buck a day alone):

- Dropped Claude Sonnet entirely - tested both models on the same data, Haiku actually performed better at a third of the cost

- Started batching everything - hourly calls were a money fire

- Filter before the AI - "lol" and "thanks" are a lot of online chatter. I was paying AI to tell me that's not feedback. That said, I still process agreements like "+1" and "me too."

- Shorter outputs - "H/M/L" instead of "high/medium/low", 40-char title recommendation

- Strip code snippets before processing - just reiterating the issue and bloating the call

End of the week: pennies a day. Same quality.

I'm not building a VC-backed app that can run at a loss for years. I'm unemployed, trying to build something that might also pay rent. The math has to work from day one.

The upside: these savings let me 3x my pricing tier limits and add intermittent quality checks. Headroom I wouldn't have had otherwise.

Happy to answer questions.

I’m trying to do the “voice assistant” thing fully locally: mic → model → speaker, low latency, ideally streaming + interruptible (barge-in).

Qwen3 Omni looks perfect on paper (“real-time”, speech-to-speech, etc). But I’ve been poking around and I can’t find a single reproducible “here’s how I got the open weights doing real speech-to-speech locally” writeup. Lots of “speech in → text out” or “audio out after the model finishes”, but not a usable realtime voice loop. Feels like either (a) the tooling isn’t there yet, or (b) I’m missing the secret sauce.

What are people actually using in 2026 if they want open + local voice?

Is anyone doing true end-to-end speech models locally (streaming audio out), or is the SOTA still “streaming ASR + LLM + streaming TTS” glued together?

If you did get Qwen3 Omni speech-to-speech working: what stack (transformers / vLLM-omni / something else), what hardware, and is it actually realtime?

What’s the most “works today” combo on a single GPU?

Bonus: rough numbers people see for mic → first audio back

Would love pointers to repos, configs, or “this is the one that finally worked for me” war stories.

It is now common to have multiple people using their smartphones to video the same event. I'm thinking Pretti and Good's killings. I've heard of Gaussian Splattering, which constructs a 3D scene from multiple cameras. Is it useful for these analyzing these events? And, if so, can someone build an easy-to-use open source tool?

My speculation is that it would be useful to: (1) synchronize video, (2) get more detail than a single camera can get, (3) track objects (like Pretti's gun) that are seen by multiple cameras, and (4) identify AI generated video.

The last is most important to me. There is a danger of AI generated or modified video of an event. It seems possible to me that Gaussian Splattering from N videos will be able to detect if the N+1 video is consistent or inconsistent with the scene.

Is this possible?

I've been trying to get agentic coding to work, but the dissonance between what I'm seeing online and what I'm able to achieve is doing my head in.

Is there real evidence, beyond hype, that agentic coding produces net-positive results? If any of you have actually got it to work, could you share (in detail) how you did it?

By "getting it to work" I mean: * creating more value than technical debt, and * producing code that’s structurally sound enough for someone responsible for the architecture to sign off on.

Lately I’ve seen a push toward minimal or nonexistent code review, with the claim that we should move from “validating architecture” to “validating behavior.” In practice, this seems to mean: don’t look at the code; if tests and CI pass, ship it. I can’t see how this holds up long-term. My expectation is that you end up with "spaghetti" code that works on the happy path but accumulates subtle, hard-to-debug failures over time.

When I tried using Codex on my existing codebases, with or without guardrails, half of my time went into fixing the subtle mistakes it made or the duplication it introduced.

Last weekend I tried building an iOS app for pet feeding reminders from scratch. I instructed Codex to research and propose an architectural blueprint for SwiftUI first. Then, I worked with it to write a spec describing what should be implemented and how.

The first implementation pass was surprisingly good, although it had a number of bugs. Things went downhill fast, however. I spent the rest of my weekend getting Codex to make things work, fix bugs without introducing new ones, and research best practices instead of making stuff up. Although I made it record new guidelines and guardrails as I found them, things didn't improve. In the end I just gave up.

I personally can't accept shipping unreviewed code. It feels wrong. The product has to work, but the code must also be high-quality.

Alibaba has officially launched Qwen3-Max-Thinking, a trillion-parameter MoE flagship LLM pretrained on 36T tokens—double the corpus of Qwen 2.5—and it’s already matching or outperforming top-tier models like GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro across 19 authoritative benchmarks. Its two core technical breakthroughs are what truly set it apart.

First, Adaptive Tool Calling: No manual prompts are needed—it autonomously invokes search engines, memory tools, and code interpreters based on task demands. This cuts down on hallucinations and boosts real-time problem-solving; for instance, coding tasks trigger automatic error correction loops, while research tasks combine search with context synthesis. Second, Test-Time Scaling (TTS): It outperforms standard parallel sampling by refining reasoning through iterative insights, with measurable jumps in key benchmarks—GPQA rose from 90.3 to 92.8, LiveCodeBench v6 hit 91.4 from 88.0, and IMO-AnswerBench climbed to 91.5 from 89.5.

Notably, its preview version even achieved 100% accuracy in tough math contests like AIME 25 and HMMT 25. The model runs smoothly on web/desktop demos, and its API is production-ready with adjustable thinking budgets (up to 80K tokens by default) to balance depth and speed. This isn’t just an incremental update—it’s a leap that closes the gap in reasoning and tool integration for real-world academic and engineering tasks.

Check it out: https://chat.qwen.ai/

Ideally, it would not only be just a pdf reader but also have functionality to remove pages, add pages, sign, and edit forms.

Hi,

I started Muky in April 2024. Classic side project that got out of hand. We have two kids - the younger one is happy with the Toniebox, but our older one outgrew it. She started asking for specific songs, audiobooks that aren't available as figurines, and "the music from that movie."

We had an old iPad Mini lying around and already pay for Apple Music. Felt dumb to keep buying €17/$20 figurines for 30-45 minutes of content when we have 100 million songs.

Now at version 4.0 after ~20 updates. Some lessons:

On the hardware vs app tradeoff: Toniebox and Yoto are brilliant for little ones – tactile, simple, no screen needed. But they hit a wall once kids want more. And handing a 5-year-old Apple Music means infinite scrolling and "Dad, what's this song about?" Muky sits in between – full library access, but parents control what's visible.

On sharing: Remember lending CDs or cassettes to friends? Or kids swapping Tonie figurines at a playdate? I wanted that for a digital app. So I built QR code sharing. Scan, import, done. And unlike a physical thing – both keep a copy.

On onboarding: First versions: empty app, figure it out yourself. Retention was awful. Now: 4-step onboarding that actually guides you. Should've done this from the start.

On content discovery: 100 million songs sounds great until you have to find something. Parents don't want to search – they want suggestions. Spent a lot of time building a Browse tab with curated albums and audiobooks for kids. Finally feels like the app helps you instead of just waiting for input.

On going native: Went with Swift/SwiftUI instead of Flutter or React Native. No regrets - SwiftUI is a joy to work with and performance is great. Android users ask for a port regularly. No capacity for that now, but Swift for Android is progressing (https://www.swift.org/documentation/articles/swift-sdk-for-a...). Maybe one day. CarPlay is another one parents keep asking for – going native should make that easier to add, if Apple grants me the entitlement.

On subscriptions vs one-time: Started with one-time purchase. Revenue spikes at launch, then nothing. Switched to subscription – existing one-time buyers kept full access. Harder to sell, but sustainable.

Ask me anything about indie iOS dev or building for kids. App is at https://muky.app if you're curious.

While many are focusing on AI chatbots / {x} AI for B2B, I am particularly excited about useful technology that can now be built in small teams thanks to AI (hardware telemetry platforms , new encrypted messaging apps, etc)

I had a VC call that went well. They asked for a demo, mentioned looping in an operating partner, and I shared details etc. Since then it’s been quiet (a day or two).

For folks who’ve raised before or worked in VC: Is this typically just internal review time, or does silence after a demo usually signal a pass?

Not looking for validation, just trying to understand how this phase usually plays out.

Thanks.

I have the following, in no particular order:

1. Languages (natural e.g. English, and formal e.g. Mathematics, Python etc) 2. Music 3. Cuisine 4. Transistors 5. MS Excel 6. Rockets 7. P2P file sharing 8. Encryption

What do you think? I think I'm missing historical inventions e.g. Gutenberg press

I’m curious how many of you have felt this:

You’re in production. The change is “simple”. A small UPDATE or DELETE with a WHERE clause you’ve read over multiple times.

Still, right before hitting enter, there’s that pause.

Not because you don’t know SQL. Not because you didn’t think it through. But because you know: •If this goes wrong, it’s on you •Rollback isn’t always clean or instant •And the safest option is often… “don’t touch it”

In reality, I’ve seen people deal with this by: •Manually backing up data “just in case” •Having someone else stare at the query with them •Restricting who’s allowed to run anything at all •Or simply avoiding fixing things directly in prod

I’m not asking for best practices or tooling advice.

I’m genuinely curious:

What do you personally do, when you have to change data and can’t be 100% sure it’s harmless?

Is this just an unavoidable part of working with production databases?

Seeing a lot of people running coding agents (Claude Code, etc.) in custom sandboxes Docker/VMs, firejail/bubblewrap, scripts that gate file or network access.

Curious to know what's missing that makes people DIY this? And what would a "good enough" standard look like?

Hello :-)

I have a Discord server I set up a long time ago. Around 2016 I think. Back then, it was lively and active and loads of fun. Over time it's developed close to 5,000 members (it actually had over 5,000 members at one point) and currently has 501 members online as I type this. It's more likely there's about 10-15 that are paying attention to anything happening.

It's a Discord that originally focused on DevOps. It complemented my YouTube channel on the same topic, but since then, as it's slowly died out, and my channel's focus as shifted and changed, it's become a bit of a waste land.

It's a shame really, because a really fun Discord server can be a great place to be, but I'm not sure where to take it now.

How would you handle this situation? What would be your approach to reviving the Discord and perhaps trying to get a community of like minded hackers going again in 2026?

I won't link the Discord here as I'm not trying to beg for users or spam. I just genuinely want to work on a solution to improve the life of the server. I will put it in my HN profile, though, so if you do want to check it out that extra step is required.

Are people even interested in Discord servers any more? I don't know.

Thanks in advance.

Built Node.js 18.20.4 natively on a jailbroken iPhone 12 Pro Max (iOS 16.5, Dopamine) to run Claude Code - no SSH to a server.

Key discoveries: - Apple's -fembed-bitcode flag added 11MB overhead per .o file vs 58KB actual code, bloating V8 libraries to 6.8GB+4.5GB - Removing the flag from common.gypi dropped the build from 29GB to under 8GB - Required fake xcrun/xcodebuild scripts since iOS has no Xcode - Every build tool (mksnapshot, torque, etc.) needs ldid signing mid-build - Final binary needed --no-wasm-code-gc flag due to iOS memory management quirks

Final result: 71MB node binary with full WebAssembly + ICU support, Claude Code running interactively.

Video: https://www.youtube.com/shorts/NXgqi1jgSq0

What are your top picks of some great launch videos recently which aren't just cool cinematically but also bring across what they are trying to solve more clearly?

Hi, everyone!

I built SHDL (Simple Hardware Description Language) as an experiment in stripping hardware description down to its absolute fundamentals.

In SHDL, there are no arithmetic operators, no implicit bit widths, and no high-level constructs. You build everything explicitly from logic gates and wires, and then compose larger components hierarchically. The goal is not synthesis or performance, but understanding: what digital systems actually look like when abstractions are removed.

SHDL is accompanied by PySHDL, a Python interface that lets you load circuits, poke inputs, step the simulation, and observe outputs. Under the hood, SHDL compiles circuits to C for fast execution, but the language itself remains intentionally small and transparent.

This is not meant to replace Verilog or VHDL. It’s aimed at:

- learning digital logic from first principles

- experimenting with HDL and language design

- teaching or visualizing how complex hardware emerges from simple gates

I would especially appreciate feedback on:

- the language design choices

- what feels unnecessarily restrictive vs. educationally valuable

- whether this kind of “anti-abstraction” HDL is useful to you

Repo: https://github.com/rafa-rrayes/SHDL

Python package: PySHDL on PyPI

Thanks for reading, and I’m very open to critique.

For example, Android builds steer towards using `sdkmanager --licenses`.

Suppose I get a preconfigured VPS with Claude Code, and ask it to make an android port of an app I have built, it will almost always automatically downloads the sdkmanager and accepts the license.

That is the flow that exists many times in its training data (which represents its own interesting wrinkle).

Regardless of what is in the license; I was a bit surprised to see it happen, and I'm sure I won't be the last nor will the android sdk license be the only one.

What is the legal status of an agreement accepted in such a manner - and perhaps more importantly - what ought to be the legal status considering that any position you take will be exploited by bad faith ~~actors~~ agents?

I recently had a conversation with an engineer who optimized assembly lines at Black & Decker in 1981 using an Apple II.

He described how they measured atomic hand movements (reach, grasp, orient) in decimal seconds to balance the line. But he made a distinction that stuck with me:

Back then, the goal was Flow (smoothness), which inherently required some slack in the system. Today, he argued, the goal of modern management is Utilization (removing every micro-second of downtime).

His quote: "We deleted the 'waiting,' but we forgot that the waiting was the only time the human got to breathe."

I feel like I see this exact pattern in Software Engineering now. We treat Developer Idle Time as a defect to be eliminated by JIRA tickets, rather than the necessary slack required for thinking.

Ask HN: For those who have been in the industry for 20+ years, do you agree?

Hi everyone! I'm posting this from a memory safe browser: WebKitGTK MiniBrowser compiled with Fil-C, plus all dependencies compiled with Fil-C

Still dealing with a tail of bugs, some of which look like overzealous optimizations leading to loss of pointer capability (leading to a filc panic). But it works well enough that I can say "hi" on here.

I see an urgent need for a map projection that makes Greenland look as small as possible. What are the options?

When building with autonomous / semi-autonomous agents, they often need broad local access: env vars, files, CLIs, browsers, API keys, etc. This makes the usual assumption — “the local machine is safe and untampered” — feel shaky.

We already use password managers, OAuth, scoped keys, and sandboxing, but agents introduce new risks: prompt injection, tool misuse, unexpected action chains, and secrets leaking via logs or model context. Giving agents enough permission to be useful seems at odds with least-privilege.

I haven’t seen much discussion on this. How are people thinking about secret management and trust boundaries on dev machines in the agent era? What patterns actually work in practice?

On iPad, .com sites show up, for my site:.ca.gov search. On Firefox, DDG wants to invoke a "System Handler"

Hello HN! I'm a long time lurker. It's only now that I've decided to sign up, when I read a comment by nick_m: https://news.ycombinator.com/item?id=46511892

I'd like to help Nick (and have some fun reverse engineering), alas I've got no means of reaching out to them. Nick, if you're reading this, please leave some contact info. I figure it's a long shot, but... why not try?

Hopefully this submission is within the rules. If not, please let me know and I'll delete it.

I’ve recently started integrating AI coding agents into my daily workflow (specifically when using Cursor Composer, Devin, Claude Code), and I’ve noticed a strange pattern in my behavior.

I treat the agent like the worst kind of micro-manager.

When I work with a human junior developer, I try to provide the "why" and the high-level architecture, then give them autonomy to solve the problem. If I stood over their shoulder dictating every variable name, commenting on every logic branch before it was finished, and constantly interrupting with "no, do it this way," they would (rightfully) quit within a week.

However, with the agent, I find that micro-management is actually the optimal strategy.

* I break tasks down into atomic units. * I review code block-by-block rather than feature-by-feature. * I constantly course-correct standard library choices or variable naming conventions in real-time.

And I felt that I am intruding the space of my agents while I should have just trust and let it build. It also sometimes break the seperation of tasks.

What makes me further unsettled is the kind of mental drain and internal conflict with what I have been trained in management.

So my question for you is

Do you micro-manage your agent?

or what's the best pratice?

On January 19, 2026, my Google Account was disabled for suspected "policy violation and potential bot activity." Within hours, my Google Cloud Platform account—hosting a community traffic monitoring website serving 17,000+ users—became completely inaccessible. I immediately submitted an appeal. Twenty-two hours later, Google sent an email confirming my appeal was approved and access was restored. But when I tried to log in, I hit an error that persists across every device, browser, and method: "Too many failed attempts - try again in a few hours." That was three days ago. What I've done:

Submitted account recovery form Filed GCP Account and Resource Recovery Request (officially promised 48-hour response) Contacted Google Cloud Support (they closed my case saying account recovery is "out of scope") Escalated through Google Maps Platform (P1 priority, but they can't help either) Posted on Google Cloud Community forums

The real problem isn't just the lockout. It's the cascading damage. I made a mistake: I registered the domain (mineheadtraffic.com) on the same GCP Cloud Domains account. I have a backup system running on a different domain, getting 5% of my usual traffic, because I can't redirect the original domain. I'm completely locked out of that DNS control too. So I'm in this situation:

My primary domain is unreachable 95% of my regular users can't find the service The backup site exists but people don't know about it All because I trusted Google enough to use their domain registrar

But here's what really stings: I still can't see what Google is charging me for it. I have zero visibility into:

What services are running What the current bill is When the next invoice will hit Whether I can dispute charges on an account I cannot access What happens after the December 15 deletion deadline

Google is billing a locked account. They have complete visibility. I have none. And there's no support path to fix it. The support structure is broken.

Premium Support ($15k+/month) explicitly doesn't cover account recovery Standard support requires account access (which I don't have) Free users have no escalation path Google One ($1.99/month) is the only way to reach a human When you reach a human, they tell you it's "out of scope"

It's a perfect catch-22. Every department passes responsibility. Cloud Support says it's not their problem. Billing Support says it's not their problem. Even the Maps Platform team (who were actually helpful and moved me to P1) can't help because account recovery is handled by a department that doesn't have a public escalation path. The part that feels like theft: Google locked me out of my own infrastructure, my own domain, my own billing account, and continues charging me with zero accountability. They don't have to tell me what it costs. I can't stop it. I can't dispute it. I'm just... stuck paying for something I can't see or control. I'm a paying customer of a company that claims to have world-class support. I'm not asking for special treatment. I'm asking: how is this acceptable? This shouldn't be possible. No company the size of Google should have a support architecture where locking out a paying customer results in zero escalation path and continuous billing with zero visibility. If this is working as designed, that's a problem. If it's a gap, it needs to be public knowledge so others don't make my mistake.