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Just yesterday I tried to find examples of good high information density UIs... and seems to be an impossible task.

Search engines are full to the brim with vague articles repeating each other's talking points, and exception being this blog post by Matthew Ström: https://matthewstrom.com/writing/ui-density/

Image search is no better, with largely irrelevant results.

In the age when everything is spaced out and zoned out gray on gray, what are your go-to examples of UIs that pack a lot of info?

I’ve been working on a Python async library ([Blazeio](https://github.com/anonyxbiz/Blazeio)) and stumbled into a shockingly simple optimization that makes `asyncio.Event` look like a relic.

### *The Problem* `asyncio.Event` (and similar constructs in other languages) has two nasty scaling flaws:

1. *Memory*: It allocates one future per waiter → 1M waiters = 48MB wasted.. 2. *Latency*: It wakes waiters one-by-one - O(N) syscalls under the GIL.

### *The Fix: `SharpEvent`* A drop-in replacement that: - *Uses one shared future* for all waiters - *O(1) memory*. - *Wakes every waiter in a single operation* - *O(1) latency*.

### *Benchmarks* | Metric | `asyncio.Event` | `SharpEvent` | |----------------|-----------------|--------------| | 1K waiters | ~1ms wakeup | *~1µs* | | 1M waiters | *Crashes* | *Still ~1µs*| | Memory (1M) | 48MB | *48 bytes* |

### *Why This Matters* - *Real-time apps* (WebSockets, games) gain *predictable latency*. - *High concurrency* (IoT, trading) becomes trivial. - It’s *pure Python* but beats CPython’s C impl.

### *No Downsides?* Almost none. If you need per-waiter timeouts/cancellation, you’d need a wrapper, but 99% of uses just need bulk wakeups.

### *Try It* ```python from Blazeio import SharpEvent event = SharpEvent() event.set() # Wakes all waiters instantly ```

[GitHub](https://github.com/anonyxbiz/Blazeio)

Would love feedback, am I missing a critical use case?

I just wanted to hear peoples experiences with AI IDEs.

For context, I'm a heavy user of Gemini / ChatGPT for coding and Copilot. But I haven't used Cursor / Windsurf / etc..

Copy pasting into chat apps is a first world problem: it will do the work for you, but you have to give it all the context in the prompt, which for a larger project, gets tedious.

The issue with Copilot is that it's not as smart as the "thinking" chat apps.

This makes it clear why there's such a need for AI IDEs. I don't want to construct my context to a chat app. The context is already in my codebase, so the AI should pick up on it. But I also hear that it gets expensive because of the pay-per-use pricing, as opposed to effectively unlimited prompts for a thinking chat app if you pay the monthly subscription.

So I just wanted to get the lay of the land. How good are these IDEs on constructing your context to the LLMs? How much more expensive is it, and is it worth it for you?

Why or why not?

The leadership of the company I’m at is forcing all teams to do 1 week sprints instead of 2 because they believe that they can get more out of the teams and get better visibility into progress. We’ve debated for months that teams aren’t getting enough upfront time to plan while still taking on the same meeting load as 2 week sprints basically every week. All the engineers, leads and product leads want to as well. I don’t know what to do anymore. All the teams are getting burned out from meeting overload and not enough prep time. Any advice?

Hey all,

We are happy to release a new benchmark for computer use. We didn’t set out to build a benchmark but found the current state of OSWorld to be very challenging to work with and numerous tests were faulty.

OSUniverse aims to be dead simple to use, it only requires docker and can run in a single command. It offers test levels that increase in complexity and are easy to extend.

We have benchmarked all the top agents. As new GUI agents are released we will continue to update their performance.

Enjoy!

Why do websites do this? Isn't it trivial for bots etc to easily side-step any protection this might give? Or is it just to make people "double-check" their inputs? I find it completely maddening because I would assume I'm way more likely to correctly input a copy-pasted e.g. routing or account number than if I have to manually enter anything. Can we just agree this is terrible UI and is probably detrimental to correct data entry? What am I missing? In frustration I even sometimes open the dev tools and remove this attribute on elements out of spite.

I've recently seen some teams claim to use third-party AI assistants like Claude or ChatGPT for coding. Don't they consider it a problem to feed their proprietary commercial code into the services of these third-party companies?

If you feed the most critical parts of your project to an AI, wouldn't that introduce security vulnerabilities? The AI would then have an in-depth understanding of your project's core architecture. Consequently, couldn't other AI users potentially gain easy access to these underlying details and breach your security defenses?

Furthermore, couldn't other users then easily copy your code without any attribution, making it seem no different from open-source software?

We’re developing an administrative app designed to simplify tasks like invoicing, purchases, inventory, and expense tracking for small businesses. Our goal is to save time and reduce stress for busy entrepreneurs while providing a very affordable subscription model. We know it's boring and not AI related but somebody has to do it and that's us, think of it as a QuickBooks or FreshBooks competitor in a very tough market.

We’d love your input to ensure the app meets real needs, if you can spare 2 minutes to answer 3 quick questions

1. What’s your biggest pain point when managing administrative tasks (e.g., invoicing, inventory, finances, paperwork)?

2. What tools do you currently use, and what do you wish they did better?

3. Would a streamlined, all-in-one app for these tasks be valuable to you? Why or why not?

We’d really appreciate your honest thoughts as a small business owner or possible investor

Thank you for helping us build something truly useful

Adminix Team

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As a thank-you, respondents get early access to the app, just select the free plan and register with your email

Hey Hacker News community! I’m Hui, the maker of AI Summarizer – stoked to share this with you today!

I built this tool because, like many of you, I was drowning in endless articles, hour-long YouTube tutorials, and dense PDFs. One day, while scrambling to prep for a project, I thought: “What if AI could cut through the noise and give me the gist in seconds?” That spark led to months of coding, testing, and collaborating with beta users to polish a tool that’s fast, free, and works in 15 languages .

Why you’ll love it: Zero cost, zero fuss – completely free, no hidden limits. Your AI, your rules – pick ChatGPT, Gemini, Grok, DeepSeek, Qwen or others to match your style. Works everywhere – summarize YouTube videos (yes, even with subtitles!), blogs, PDFs, and more. Speaks your language – whether you’re reading in Hindi, Korean, French, or 12 others.

Try it now and let me know: What content do YOU struggle to digest? Which AI model gives you the best summaries? Any features you’d love to see next?

Big thanks to early testers who helped shape this – your feedback is gold! Let’s keep the conversation going: Ask me anything about the tech, roadmap, or how to tweak prompts for laser-focused summaries. Let’s make information overload a thing of the past!

Install here: https://chromewebstore.google.com/detail/ai-summarizer-web-youtube/dcohpcjocgijndjcachlifniaendimid Cheers to working smarter, not harder!

P.S. Share a screenshot of your first summary – I’d love to feature your vibe!

I’m building a system to run small LLMs on-device (mobile, IoT, on-prem servers) and would love to hear how others have tackled the challenges.

Context:

Use cases: offline chatbots, smart cameras, local data privacy

Models: 7–13B parameter quantized models (e.g. Llama 2, Vicuna)

Constraints: limited RAM/flash, CPU-only or tiny GPU, intermittent connectivity

Questions:

What runtimes or frameworks are you using (ONNX Runtime, TVM, custom C++)?

How do you handle model loading, eviction, and batching under tight memory?

Any clever tricks for quantization, pruning, or kernel fusions that boost perf?

How do you monitor and update models securely in the field?

Looking forward to your benchmarks, war stories, and code pointers!

Sales teams often struggle with limited visibility into their calls, reviewing only 5-10% manually, which leads to missed opportunities. We built an AI-powered voice analytics tool that transcribes, indexes, and analyzes 100% of calls, turning them into actionable insights. In one case, this helped a SaaS client grow sales by 120% in 12 months.

What the tool does

We aimed to provide non-intrusive, automated QA at scale. So the key features include: - 100% call transcription: using ASR for accurate, fast transcriptions. - Searchable database: indexed transcripts for easy keyword and phrase tracking. - Customizable reports: automated manager reports, grouped by agent or team. - CRM integration: syncs data to tools like Salesforce and Zoho.

Limitations: currently lacks real-time alerts, sentiment analysis, and emotion scoring (planned for future updates).

Architecture overview - Audio capture: integrated VoIP or manual uploads. - ASR pipeline: transcribes calls via cloud-based speech-to-text. - Transcript indexing: elasticSearch stores and retrieves data efficiently. - Keyword matching: flags important terms like pricing or CTAs. - Reports: automated generation of weekly summaries.

Real-world impact. One SaaS client improved - 120% sales growth over 12 months. - 35% increase in close rate by identifying high-performing patterns. - 5-day reduction in sales cycle due to consistent messaging. - Churn dropped from 15% to 6% through better objection handling.

This was achieved without expanding the team — simply by leveraging the power of data.

Challenges & lessons learned - Keyword rules: over-flagging terms led to alert fatigue, so we customized per-client keyword sets. - ASR model issues: addressed by adding pre-filtering for noisy inputs and fallback models. - CRM integration: built middleware to adapt to varying CRM structures across clients. - Manager overload: simplified reports to highlight top deviations, avoiding information overload.

Next steps: what's coming

- Trend detection: analyzing keyword frequency over time. - Conversation templates: auto-tagging calls (intro, demo, pricing). - Call quality scoring: identifying poor audio or incomplete conversations.

Key takeaways - Focus on basics: transcription + search + simple flags bring massive value. - Human-in-the-loop: insights are most useful when actionable in real-time. - Scalability = simplicity: focused, simple solutions deliver better results. - Data ≠ insight: reports need to be curated and actionable for managers.

Conclusion AI is a powerful tool for sales teams, but success comes from turning raw data into actionable insights. By building scalable systems and avoiding complexity, we were able to achieve real business growth — and this approach is adaptable across industries.

After playing Octopath Traveler II, I was really inspired by its art style and decided to create my own pixel drawing app from scratch. It's still in the early stages, but it already supports basic drawing, undo/redo, and a clean UI focused on pixel-perfect design.

I plan to add frame-by-frame animation support in the next version.

You're welcome to try it out and share your thoughts! If you like the project, a on GitHub would mean a lot to me.

Live demo: https://ayanamiii.netlify.app/

GitHub: https://github.com/KamiC6238/ayanami

Share your information if you are looking for work. Please use this format:

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Readers: please only email these addresses to discuss work opportunities.

There's a site for searching these posts at https://www.wantstobehired.com.

Nvidia just announced that the GeForce RTX 5060 will hit shelves on May 19 starting at $299. It succeeds the GTX 1060/RTX 3060 line, adds DLSS 4 and Multi-Frame Generation—but still ships with 8 GB of VRAM amid ongoing supply shortages.

I’m curious: Will this price point and feature set finally revive mainstream PC builds? Is 8 GB of VRAM still enough for today’s 1080p/1440p gaming?

Looking forward to benchmarks and your early impressions once cards start rolling out!

Let's say you have a software development firm. You have some sales/marketing people and you have some developers. How do the sales and marketing people obtain projects for the developers to work on?

Please state the location and include REMOTE for remote work, REMOTE (US) or similar if the country is restricted, and ONSITE when remote work is not an option.

Please only post if you personally are part of the hiring company—no recruiting firms or job boards. One post per company. If it isn't a household name, explain what your company does.

Please only post if you are actively filling a position and are committed to responding to applicants.

Commenters: please don't reply to job posts to complain about something. It's off topic here.

Readers: please only email if you are personally interested in the job.

Searchers: try https://amber-williams.github.io/hackernews-whos-hiring/, http://nchelluri.github.io/hnjobs/, https://hnresumetojobs.com, https://hnhired.fly.dev, https://kennytilton.github.io/whoishiring/, https://hnjobs.emilburzo.com, or this (unofficial) Chrome extension: https://chromewebstore.google.com/detail/hn-hiring-pro/mpfal....

Don't miss these other fine threads:

Who wants to be hired? https://news.ycombinator.com/item?id=43858552

Freelancer? Seeking freelancer? https://news.ycombinator.com/item?id=43858553

I've just got a new macbook air and I'm re-evaluating which Firefox add-ons to use.

So far I have:

Privacy badger Unhook Firefox Containers uBlock Origin

What are your most useful ones for 2025? Anything significant that I'm missing?

Been going to a bunch of hackathons in SF lately and honestly, everything feels fake. There are like 20 sponsors handing out credits for their tools that all do the same thing. Half the time, they can’t even explain what they’re for. They’re just hoping someone uses them so they can count it as adoption. Everyone jams these into projects to check a box, and what gets built is mostly BS with zero innovation. Was it always like this and I'm noticing it now, or has something changed?

I’ve been deep in Helm templates lately and it’s starting to feel like YAML hell. It was fine when we had a few services, but now it’s just hard to manage. Anyone found a workflow that avoids Helm altogether? Or made Helm manageable at scale?

When did this UX convention develop and why? I think this is also true across languages like Arabic and Hebrew whose script runs right-to-left.

I was going to buy a component on Aliexpress and noticed this at checkout: "Item not deliverable to the selected country/region". Did anyone else notice this? Is this because the seller doesn't want to deal with possible customs clearance issues? I have previously bought from this seller without this issue in the past.

I did a thought experiment. The full experiment is at bottom. Here’s the core:

How can you build a website that doesn't host code, just instructions on how to find it.

If you're a seasoned dev, the answer is obvious, it's instructions all the way down. That is why we have JS, go, rust.

Information transmission is extremely important.

Apps on your phone, like Facebook, allow the company to serve the entire website with the UI kit and logic on your phone, and the UGC (text and images) served from FB, drastically cutting down the amount of info they have to send over the server. It's a huge cost cutting manoeuvre for them to serve smaller server responses to billions of daily users who are accessing new content every second.

“Serverless” deployment where you host and serve content and instructions, and the app on the users phone is what does all the logic is essentially the idea behind the fediverse.

There's often a push from business to make things bigger and cooler, and then when they slow things down, to make them faster.

But IMO the transformative "magic" concept is just making things less reliant on infrastructure. Sometimes I want to talk to my friends in person.

I’m really curious to know what is going on in this sector and what people think about it.

THOUGHT EXPERIMENT

Build a compiler that takes a line of instructions that tells the client how to search through the web for snippets to compile the DOM.

Here are some issues

Problem 1: Instructions It's essentially a compression problem. Every pathway to efficient compute can end up here. What is the most efficient way to write a piece of code. Then what is the most efficient way to store it. Can a single byte of code spring into 1000 different algorithms. Mathematically, I'm skeptical. A short int is 2 bytes, so getting below 1 "instruction set" per bit is tough.

Problem 2: Searching How do you know where to look in a way that results in fast code

Problem 3: Uniqueness How do you know, in a practically infinite internet, whether something is unique or not?

Problem 4: Searching Okay so you have your super compression Algo that can take a Blue sky string of hexcode and spit out a viable set of search instructions to an interpreter, that can then be found and expand into an even larger website.

How do you find it? If you aren’t rate-limited by search engines for hammering their sites for little pieces of code, you have to do a bunch of expensive, time intensive search.

Ping the server, go to the address, search the dom. Return pass or fail. Keep going.

You can "fix" for this by having some sort of Borges infinite library that you can search through.

Real use case: Stick instructions on a QR code, scan with your phone, load a website that is not hosted on a server, but instead fed directly to your phone by the code. ??? Profit

Result: There are better ways to achieve the above use case. Compress a website into a few pages of QR codes, make a video that can wipe across them and load it.

Information transmission and storage.

If you lean too heavily into efficiency, you shoot yourself in the foot because efficiency borrows from other systems.

The USDOT does repair to upgrade. Standards are rolled out as fast as things break. Things are constantly breaking, so things are constantly getting upgraded.

This speaks to the difficulty to which propagating new changes is really hard.

Information and transmission aren't free. Philosophically yes, but materially, it’s expensive.

You have to get into the nitty gritty of developing not just languages, but entire storage, transport, distribution systems of information. Like any "globally perfect" system, it's a network of different players all at cross purposes, designing different standards for each other.

So here we are, back where we started, with an extra iota of clarity.

If you do use it, how does it fit into your workflow?

Hi -- I've posted before about a director of mine, who joined a few months ago. I'm trying to get a sense of how to interpret this situation, and what (if anything) there is to do for me here.

The director has a reasonably strong background in a specialty we're hiring for. We haven't put dedicated effort into this specialty, and only two of us (me and a staff-level teammate) have a background in this specialty. This specialty is a strong focus of the director, even to the point of excluding other very important areas of work.

We've been interviewing candidates, targeting those with this specialty. My interview is dedicated to assessing candidates' facility and depth with this specialty. I'm sensing what seems like some amount of distrust from the director about me and my team's capability to hire good candidates for this specialty.

In one case, my team did an interview loop with a candidate and decided to approve. The director wanted to meet with the candidate individually after the fact to assess whether they would indeed be a good fit, rather than as a "sales" call when an offer is extended.

In another case, the director second-guessed an internal candidate whom my manager and I fully supported joining the team; in this case, the director didn't interview the internal candidate, but had expressed skepticism about their suitability for the team, despite our strong support, and background (though not especially recent) in this specialty.

This is happening again in a third case -- a candidate passes the loop, but the director wants to meet to do at least some amount of assessment.

Is the director distrusting of the team (and hence of me in particular)? I've met with him a few times, and he's insistent about some pieces of work (prematurely, I think -- there's still a good amount of fact-finding to do before deciding on what, and when, to work on different options). I get the feeling he thinks our team isn't great at this, since it hasn't been urgent and prioritized before.

Are there interpretations I could be missing here? Could this be just a matter of style, that the director (~4 teams, ~30 reports, though growing) wants to be very hands-on with hiring, even ~mid-level candidates? If the director's doing any kind of assessment of the suitability of candidates after the loop, I'd have to assume the director would find it feasible to veto a candidate, even having a full loop approving.

If the director probably is distrusting, then besides delivering on this specialty myself, any ways I can earn the director's trust? I plan on delivering wins in this area myself, though I can't guarantee I can commit enough heads-down time to this; and my director and I so far haven't really seen eye to eye on approaches to tackling this.

Thoughts, comments, experiences welcome. Thanks.

After spending a few months building out a new project that relies on the Instagram Graph API I'm struggling to pass Meta's Access Verification for Tech Providers.

We passed the app verification and business verification fairly easily, but we're stuck getting access verification. It feels like Meta is automatically denying us access without telling us explicitly what they are looking for or what we are missing from our answers. We keep getting knocked back with the same reply asking for:

   1. Add details about how your business will use Platform Data (i.e., any info or data you obtain from us) to enable a product or service on behalf of your clients.

   2. Describe how your clients use your product or service.

Has anyone had experience with this and can shed some light on what I might be doing wrong?

Thanks!

Hello HN,

Since a young age, I've been interested in machine and deep learning. I’m currently in the second year of my Computer Science BSc (Toronto, Canada) and already have almost 2 years of experience in industry (computer vision + NLP) and over a year in academia doing AI research (both full time). Additionally, I have quite a few open-source projects (all DL-related) that have garnered over 1,000 stars in total, and some are very well-known in their respective niches. Lately though, I'm getting the impression that the field is over-saturated, with new research being published on a daily basis, and I identify as nothing but a cog in the machine as an AI developer. I understand that all domains are affected by this phenomenon to some degree, but in AI in particular, my work doesn’t feel personal at all, and to myself, I ironically seem like a robot that trains a vision transformer to do classification, fine-tunes an LLM for certain types of documents, makes architectural changes for a tiny improvement in performance, etc.

What are alternative branches in CS that you suggest I consider? I have two chief priorities:

* Creativity: I'm not seeking a typical software development job such as full-stack developer. Instead, I'm interested in opportunities that require creativity, almost like puzzle solving, and don’t become “routine” after a while. * Industry: My goal is to work in industry, not academia. This is not because I don’t enjoy research (in fact, I prefer it to applied work), but as reluctant as I am to admit it, salary does play a role in my decision making, and I’m aiming for six figures.

To give you a concrete example: I love work in logic, programming language research, theoretical computer science, and so on because they satisfy my first criterion, but it sadly appears that employment opportunities are mostly confined to academia?

I really appreciate your thoughts and feedback.

I'm a young researcher with a background in abstract physics and mathematics, specializing in areas like probability theory, spectral analysis, network theory, and statistical/quantum mechanics.

I'm considering transitioning to industry, but my coding experience is limited to basic Python. I’m concerned that, with a strong theoretical background but limited engineering skills, I might struggle to find a good fit. Additionally, with the tightening job market, the abundance of remote talent, and the rise of tools like Copilot, I’m wondering how these factors might impact my prospects.

For those who’ve made a similar transition—or who have hired people with this kind of background—how did it go? What roles or paths have worked best for you?

The Knowledge Graph Conference is currently happening in NYC and there's a bit of talk around KG assisting AI with various things like truth grounding (RAG) and Agentic development. Curious if anyone is seeing more discussions of Semantic Technologies in their orbit these days?