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For those of you who ship often, when you release updates, do you typically write one set of release notes, or do you end up rewriting them for different audiences?

For example: • technical version for developers • simplified version for end users • something more high-level for stakeholders etc…

In my current position I’ve seen a plethora of different ways teams, and even the company I currently work for, go about this.

What I’ve seen: 1. paste raw GitHub changelogs into customer emails (highly wouldn’t recommend if you’re currently doing this ) 2. manually rewrite the same update multiple times for each audience 3. skip release notes entirely because it’s too much work

So I guess my question is: How do you or your company currently go about handling more than one set of release notes, and do you feel like more than one set is needed?

Would love to hear what’s working (or not working) for you, and if you found any tools that help mitigate this issue.

I've been working on a personal project recently, rewriting an old jQuery + Django project into SvelteKit. The main work is translating the UI templates into idiomatic SvelteKit while maintaining the original styling. This includes things like using semantic HTML instead of div-spamming, not wrapping divs in divs in divs, and replacing bootstrap with minimal tailwind. It also includes some logic refactors, to maintain the original functionality but rewritten to avoid years of code debt. Things like replacing templates using boolean flags for multiple views with composable Svelte components.

I've had a fairly steady process for doing this: look at each route defined in Django, build out my `+page.server.ts`, and then split each major section of the page into a Svelte component with a matching Storybook story. It takes a lot of time to do this, since I have to ensure I'm not just copying the template but rather recreating it in a more idiomatic style.

This kind of work seems like a great use case for AI assisted programming, but I've failed to use it effectively. At most, I can only get Claude Code to recreate some slightly less spaghetti code in Svelte. Simple prompting just isn't able to get AI's code quality within 90% of what I'd write by hand. Ideally, AI could get it's code to something I could review manually in 15-20 minutes, which would massively speed up the time spent on this project (right now it takes me 1-2 hours to properly translate a route).

Do you guys have tips or suggestions on how to improve my efficiency and code quality with AI?

Here's a video from 2004 https://www.youtube.com/watch?v=Y1J9kfDCAmU

It's his 100th birthday today.

It seems Meta recently migrated all Messenger chats to e2ee. But if you had created a secret conversation (e2ee) in the past then instead of migrating the existing chat Meta decided to archive and switch to the secret chat.

Since I only experimented with/created secret chats in the past with close friends and family, all my old conversations with years of history were archived with no path to restore.

This is a horrible UX experience and absolutely ridiculous that this design passed in Meta.

I understand why Ai is dominating online discourse right now. The tech is novel, it’s pushing boundaries, the business side has trillions of dollars involved, and it’s made its way to the mainstream of every day people.

But, I just truly don’t find it interesting. For all those that do - great! But for myself, for whatever reason it just does not scratch that part of my brain. I’d rather spend days writing and debugging code (to create a 5 minute automation ;) ) than having Ai spit something out in 10 seconds.

I just use Ai like a supercharged stack overflow. Ask it something if I have a syntax error or whatever, and then move on by continuing to use my own brain to think through the logic and patterns of my project.

All that to say - I miss what HN was before Ai and LLMs started dominating everything!

Anyone have other spaces, blogs, communities, or whatever where you go to learn and/or discuss interesting things that don’t have anything to do with Ai?

I read many non-fiction books, but recently noticed that only a few qualify as truly heavy, thought-provoking reads, that you literally can't finish in a manageable time because you keep telling yourself, "Wait a minute," then stop to Google something, run an experiment, or just think deeply. My current example (still unfinished) is "Moonwalking with Einstein" by Joshua Foer. It's mind-blowing - the entire memory universe around us that I never properly explored before.

Working on automating small business finance (bookkeeping, reconciliation, basic reporting).

One thing I keep noticing: compared to programming, accounting often looks like the more automatable problem:

It’s rule-based Double entry, charts of accounts, tax rules, materiality thresholds. For most day-to-day transactions you’re not inventing new logic, you’re applying existing rules.

It’s verifiable The books either balance or they don’t. Ledgers either reconcile or they don’t. There’s almost always a “ground truth” to compare against (bank feeds, statements, prior periods).

It’s boring and repetitive Same vendors, same categories, same patterns every month. Humans hate this work. Software loves it.

With accounting, at least at the small-business level, most of the work feels like:

normalize data from banks / cards / invoices

apply deterministic or configurable rules

surface exceptions for human review

run consistency checks and reports

The truly hard parts (tax strategy, edge cases, messy history, talking to authorities) are a smaller fraction of the total hours but require humans. The grind is in the repetitive, rule-based stuff.

I was sad to discover today that all of my Dall-E image generations are gone, along with the entire https://labs.openai.com/ site. Apparently, some users received emails earlier in the year when it was about to be taken down, but I didn't. There were quite a few images in my history that I would have liked to have saved.

Maybe worse is how much this lowers my trust in OpenAI even further than it already had been. Dall-E was not a small platform; it was a cultural phenomenon accessed by hundreds of millions of users. It's bewildering that OpenAI would so silently "take it behind the shed."

I'm searching, and it doesn't seem as if there was even an HN post about the shutdown. So, it didn't even hit this place's radar. How many of you are hearing this for the first time?

I don't understand how a company like OpenAI could be so reckless with user data integrity and access, particularly when sunsetting a product. All of the big-dog tech platforms have fairly robust protocols for notifying users and allowing them to download their data (even with hoops to jump through). How can they hope to be one while still acting like a "move fast and break things" startup? I liked the thing they broke.

Got this scam email about an opportunity to earn passive income by acting as the front for an employment fraud scheme.

How does the scammer benefit from this operation?

I can think of 2 ways:

- Personal / private data mining, but this seems quite work intensive for that purpose - Actually going through with the whole scam and disappearing after first salary payments come through

Any other ideas? Anyone have experience or insight about this?

---

Full email below:

"Hi <name>, I hope you’re doing well.

My name is <sender>, and I’ve been a software developer for over 7 years — mainly full stack, with a strong focus on frontend. I’m reaching out because I’m looking for someone to collaborate with, and I think you're the best one whom I'm looking for.

I used to partner with my friend Jim, and we worked really well together. Sadly, he was diagnosed with cancer about a month ago, and he advised me to find someone new to team up with. That’s why I wanted to talk to you.

Here’s the idea: I’ll handle sending proposals to companies, and you would take care of the interviews with recruiters. My English isn’t strong enough for U.S. interviews, so I’m looking for someone who is confident in English and also has strong technical knowledge. If we land a position, you’d receive a share of the monthly salary, while I would take care of the actual development work.

My initial suggestion is a 50/50 split of the salary after tax. For example, if a job pays $10K per month and taxes are 30% ($3K), the remaining $7K would be split evenly — $3.5K each.

I will manage all the proposals and keep you informed about interview schedules. If things work out and we join a team, I’ll handle the project development. Then you'll get profit every month without any work.

If this sounds interesting to you, please let me know — I’d really like the chance to work together.

Thank you, Best regards, <sender>"

As various LLMs become more and more popular, so does comments with "I asked Gemini, and Gemini said ....".

While the guidelines were written (and iterated on) during a different time, it seems like it might be time to have a discussion about if those sort of comments should be welcomed on HN or not.

Some examples:

- https://news.ycombinator.com/item?id=46164360

- https://news.ycombinator.com/item?id=46200460

- https://news.ycombinator.com/item?id=46080064

Personally, I'm on HN for the human conversation, and large LLM-generated texts just get in the way of reading real text from real humans (assumed, at least).

What do you think? Should responses that basically boil down to "I asked $LLM about $X, and here is what $LLM said:" be allowed on HN, and the guidelines updated to state that people shouldn't critique it (similar to other guidelines currently), or should a new guideline be added to ask people from refrain from copy-pasting large LLM responses into the comments, or something else completely?

Hi guys,

In middle of a stripe Shakedown and feel like I this is something to warn others of.

We rent vehicles and implemented stripe in 2017. We process a massive volume and must have spent 1B+ with stripe so far.

We love stripe, the tools the software ect. But recently they have been closer to a mob boss than a vendor as they must know their customers are highly locked in.

Over time, our deal evolved into a multi‑year minimum annual fee commitment with “enterprise” pricing. On every renewal, the pattern has been:

Stripe pushes the minimum annual fees higher.

If we don’t naturally hit that minimum, they expect us to burn the difference on add‑on products and “nice to have” features just to satisfy the commit.

We’re warned that if we don’t find a way to hit the minimum, they can just take the full amount out of revenue.

What I would think of before picking stripe.

Make your integration portable. Don't use vender form/card logic. 2. Use an invoices platform that can easily switch card provider. 3. Push back on the small yearly minimum, as they will just raise it next time instead of focusing on you making more money stripe focuses on itself.

To anyone working at stripe, you guys built an awesome product, just wish you could maintain the culture that got you to where you are.

Good luck

Hi Y Combinator community and peers,

What is the best Cybersecurity bootcamp/jumpstart to get the foundation and start a career?

I'm searching for courses that are intermediate or advanced since I already have a foundation with software development in C++/js/python.

Thank you for your attention.

Every word, sentence, and phrasing choice reveals more than just your thoughts. It can show your education, whether English is your first language, how much you’ve read, and even aspects of your intelligence.

So in a way, writing without AI on social media can be even more exposing than posting your medical records online.

What do you think?

After many years of working hard, my wife and I finally decided to have a kid recently. I am looking forward to spending a lot more time with her, while working just a 9-5. However, the AI boom seems like a short-lived, once in a life-time opportunity. Now I am considering if I should dive in fully even if it means sacrificing time with family?

Options: 1) Joining an AI startup 2) Founding an AI startup 3) Stay at my big-tech role

I make good money at my current role, but I am extremely passionate about the work I do. With the AI boom, there seems to be a lot of great infra, data movement and algorithmic challenges on the table. However, it is also clear that options 1 and 2 seem to require a lot more time commitment than a simple 9-5 job.

There are also financial considerations. If AI affects the SWE job market in a meaningful way, my future salary will be drastically low. Maybe it makes sense to devote myself to work for 3-5 more years and make a lot more money in the short term? I understand that there is also some probability associated with the final amount I walk away with.

Any others thinking about how to approach the AI boom vs leaning in fully to parenthood?

Hi! I'm pre-revenue but there's a buzz in the corporate events industry about my AI Agent MVP. Who do you recommend to help me get my slide deck and financials ready for fundraising? What is the best way to fundraise for seed capital when pre-revenue? Family Offices in the Middle East (because the US is sluggish on startup investing right now)? "Go Fund Me" type sites for B2B products? Something else?

Hello,

Since 10th December, Substack introduced age verification to login to your account. Unfortunately this breaks the process of deleting an account (which requires you to login).

How can I delete my account without completing the video streamed mug shot age verification process?

support@substack.com autoreply directs you to substack.com/contact -- which also requires you to pass age verification first.

The substack AI chatbot is useless - no hope of reaching a human. The process for cancelling a deceased account doesn't work either as the chatbot can't look up my account using my email address (presumably it is frozen until age verification). Tor and VPNs don't work as my profile includes my location in Australia, and my email address is Australian.

Any ideas? I'm about to cancel my credit card as a last resort.

Throughout 2025, I've been following job boards like YC Jobs, RemoteOK, NoDesk, WeWorkRemotely, and others. Across all of them, I keep seeing a recurring pattern:

Many companies advertise "remote" roles, but hiring is limited to the US, Canada, UK, or Germany. Sometimes they add one or two more countries, but rarely anything beyond that.

Given that it's the last quarter of 2025 and remote work is more established than ever, I'm trying to understand the reasoning behind this.

A few questions I'm hoping founders, hiring managers, or people with international hiring experience can shed light on:

- Is the main blocker regulatory complexity? (employment law, compliance, local registrations, PE risk, etc.)

- Is it primarily about taxes and payroll overhead when hiring abroad?

- Are there security or liability concerns that make certain jurisdictions easier to work with?

- Is it simply the cost of maintaining compliant employment structures worldwide, or are there deeper strategic reasons?

- And finally: Is there evidence that the value produced by strong engineers abroad doesn't offset those costs, or is the issue not economic at all?

I'm asking out of genuine curiosity, from the outside, it seems like a global talent pool should be an advantage, especially for remote-first companies. But the hiring restrictions persist, even as tools like Deel, Remote, Oyster, etc. mature.

I'd love to hear perspectives from people who have dealt with this firsthand.

I have a preprint article [1] which I'm trying to get submitted to arXiv's cs.SI (Social and Information Networks) for peer review. As an independent researcher I do not have any affiliations to a university anymore. How do I go about this? Appreciate any kind of info / support.

[1] https://static.philippdubach.com/pdf/Attention_Dynamics_in_Online_Communities_Power_Laws__Preferential_Attachment__and_Early_Success_Prediction_on_Hacker_News.pdf

I was surprised to find out only recently that some credits I bought about a year a go were unusable because they had expired. I find this a bit concerning because it seems as though I'm being forced to use the credits.

In my part of the world, that tactic is used by telcos to sell "broadband data". You buy internet bundle of about $1 and they give you expiry of about a week. This drives up the "real" price of these purchases because of the time constraint. Ultimately, if you had 1GB of data left after a week, it is all gone and you have to purchase again - further driving sales. Since this is a third world country we're talking about and telco's tend to be oligopolies and tend to also do some form of price collusion among themselves, it was generally accepted as "just how things were".

But I always found it to be unfair because people should be allowed to take their time consuming whatever product they purchased.

I wonder if this is general practice for all llm apis? Am I missing something? Is this really how things should be? I can't seem to fathom why "purchased" llm credits should have an expiry date - however generous. Especially when the same credits can be used to access any of their available models.

The future of lighting is here—and it's not just about brightness. With the rise of smart LED technology, we've seen a massive shift toward energy efficiency, customization, and even integration with home automation systems.

For instance, addressable LED strips are a game-changer, allowing precise control over each segment of light for personalized ambiance. Whether you're setting the mood with colors, syncing with music, or automating lighting schedules, the possibilities are endless. But beyond aesthetics, these LEDs are changing how we think about energy consumption. With their ultra-low power consumption and customizable features, they offer both environmental and financial savings.

The latest innovations even push the boundaries of AI and IoT. Think smart lighting systems that learn your preferences over time or adjust based on natural light conditions. Have you experimented with smart LED lighting in your home or office? What features do you think could make these technologies even smarter?

Top 1 or top 1-3 book you read this year (2025) that you would recommend to the HN community? Note: book itself doesn't need to have been published in 2025.

As we know, OpenAI is not so open.

In 2023, I was playing with transformers, RNNs and I had an understanding how it worked from top to bottom (e.g. made my own keras, could whiteboard small nets) and I can throw things together in keras or tf pretty quick

I got a job and never touched that again. Data and compute notwithstanding, how hard would it be to make a pet project foundation model using the latest techniques? I’ve heard about MoE, things like that and I figure we’re not just throwing a bunch of layers and dropout in Keras anymore.

I’m trying to renew my IEEE membership, but the Payment page keeps erroring out regardless of credit card.

Worse, I can’t seem to reach anyone at IEEE through email, phone calls, or the web form. Is anyone able to get through?

1. Turn Any Screenshot into a Structured Note Screenshot (meeting notes/webpage/paper/etc.): Extract key text; Summarize as clear bullet points; Create 3–5 to-dos with due dates & owners (TBD). 2. “One‑Click” Spreadsheet Analyst “upload a spreadsheet image / CSV of your data. Describe the main patterns in plain language. Suggest 3 charts that best explain the data and describe what each chart should show. Point out anomalies or data quality issues.” 3. Context‑Aware Refactor Coach for Code

“From screenshots/pasted text, infer code architecture and file roles. Suggest a step-by-step refactor plan. For Step 1 only: provide exact code changes—await your approval before Step 2.” 4. Auto‑Generate Test Cases from Real UIs

“Upload app/web screenshots → Identify interactive elements → Generate test-case table: [Element, Expected behavior, Edge cases] → Suggest automated test ideas for Playwright/Cypress/Appium (no full code).” 5. Act as a cautious research assistant:

“Given a claim, identify likely accurate, questionable, and clearly wrong parts. For questionable points, suggest source types & keywords to verify. Rewrite the claim more accurately while preserving nuance.”

Problem with ambiguous boundaries, messy constraints and no linear path to a solution

When I was young we read books like Surely You're Joking, Mr. Feynman! by Richard Feynman, Neuromancer by William Gibson and So You Want to be a Mathematician by Paul Halmos. What books are popular with young technically minded people today?

I have been writing Python code for a few years now. But I feel like LLMs can write much better code than me. I used to keep myself updated with newer technology. But now I am loosing interest. I was interested in learning Rust. But I don't find any motivation now since I can just vibe code with Rust. Any thoughts in that?

  Hey HN! I built console.text() - a tool that texts you when specific code paths execute in production.

  The idea came from Jason Goodison's YouTube video about micro-SaaS products. I'd been stuck in tutorial hell for months, so I decided to just ship something.

  What it does:

    npm install @holler2660/console-text

    const { init } =  require("@holler2660/console-text");
    init({ apiKey: 'ct_live_xxx' });

    console.text('Payment failed', { userId: '123' });
    // → SMS arrives in 5-10 seconds

  Try it: https://soorajdmg.github.io/Console-text/

  Why this vs Sentry/PagerDuty?

  Those are great for teams. This is for solo devs and side projects who want dead-simple alerts without the setup overhead. If you know console.log(), you already know how to use it.