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Curious to know your experience running local LLM's with a well spec'ed out M3 Ultra or M4 Pro Mac Studio. I don't see a lot of discussion on the Mac Studio for Local LLMs but it seems like you could put big models in memory with the shared VRAM. I assume that the token generation would be slow, but you might get higher quality results because you can put larger models in memory.

I understand Llama 4 was a disappointment, but what's happened at Meta since then? Their API is still waitlist-only 10 months on.

I don't understand the business model of AI companies like OpenAI and Anthropic: if the claims of nearly autonomous agentic product development are true, why would they sell access to their LLMs, in some cases at a loss, instead of providing consulting services at a fraction of the cost when compared to traditional human businesses?

In other words, why would they sell AI as a sort of commodity instead of using it to provide an end product like IT consulting which is extremely profitable?

Hi HN, what was the most complex and sophisticated algorithm you came up with yourself while working on a problem?

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Looks like there's another round of Zendesk email spam happening. I've gotten hundreds over the last half-hour.

I'm seeing individual programmers who have moved to 100% AI coding, but I'm curious as to how this is playing out for larger engineering teams. If you're on a team (let's say 5+ engineers) that has adopted Claude Code, Cursor, Codex, or some other agent, can you share how it's going? Are you seeing more LOCs created? Has PR velocity or PR complexity changed? Do you find yourself spending the same amount of time on PRs, less, or more?

I have been managing websites for a while and usually utilize SSH connections to login to, deploy code to, and otherwise remotely access the hosting servers.

I was recently informed that a client I work with considers that a legal risk.

If the SSH connection is set to disallow passwords and only authorize via SSH keys, how big of a risk is this?

I have encountered ads on YouTube that closely match what I think I am only asking on ChatGPT.

For example, just today I asked ChatGPT if I am speaking too much and an hr or so later I see a sponsored video on YouTube about exposing your weakness by speaking too much.

Is there a direct link between the two, am I hallucinating or it's a divine intervention?

I am on FF on Linux and I have ChatGPT installed on my Android phone. If that makes any difference.

Thanks

Surely Microsoft wants that juicy Microsoft Office river of gold, and if cloud is banned, maybe they'll bring back the desktop?

For years, SEO has meant optimizing for Google’s crawler.

But increasingly, discovery seems to be happening somewhere else: ChatGPT Claude Perplexity AI-powered search and assistants

These systems don’t “rank pages” the same way search engines do. They select sources, summarize them, and recommend them directly.

What surprised me while digging into this: - AI models actively fetch pages from sites (sometimes user-triggered, sometimes system-driven) - Certain pages get repeatedly accessed by AI while others never do - Mentions and recommendations seem to correlate more with contextual coverage and source authority than traditional keyword targeting

The problem is that this entire layer is invisible to most builders.

Analytics tools show humans. SEO tools show Google. But AI traffic, fetches, and mentions are basically a black box.

I started thinking about this shift as: GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization)

Not as buzzwords, but as a real change in who we’re optimizing for.

To understand it better, I ended up building a small internal tool (LLMSignal) just to observe: - when AI systems touch a site - which pages they read - when a brand shows up in AI responses

The biggest takeaway so far: If AI is becoming a front door to the internet, most sites have no idea whether that door even opens for them.

Curious how others here are thinking about: - optimizing for AI vs search - whether SEO will adapt or be replaced - how much visibility builders should even want into AI systems

Not trying to sell anything — genuinely interested in how people here see this evolving.

I realize that its probably me, I'm the dumb one, but please bear with me and help me understand. I've been recently looking for a new job as I am slowly viewing my previously functioning workplace accelerating towards a static dysfunction.

I have spoken to quite a few companies and read a lot of recruitment boards in a rather sizable european city that ought to be filled with opportunities. With tech-sovreignty on everyones lips I would expect some drive and excitement in the european software scene, but to get to companies with a mission I have to wade through The Swamp. The Swamp is waist high in Scrum certifications and gigs where the key skill is "navigating red tape". There the architects roam, with no expectations of from management, and with a mandate to stop every system that does not yet include an Azure Event Hub. In the large corporations where the most important roles are the power-BI analysts and the best metric for value-creation is the fill of your calendar and your hours of overtime.

And somehow, if feel like your getting somewhere with a company that thats primarily motivated by crafting something good, not focusing on vanity metrics or micromanaging how things are done -- its going to be a marketing startup.

Summarized: Most of the businesses I see seem to be bloated. They have way to many employees for what they produce. They have too much structure and too many rules to effectively generate new income, and new ideas are shut down and not welcome.

But I genuninly do wonder: Are businesses somehow incentivised to become inefficient? Is it possible for a business to stay ambitious over time? Has one seen it succeed or how have you seen it fail?

We're a YC W23 company building AI agents for engineering labs - our customers run similar analyses repeatedly, and the agent treated every session like a blank slate.

We looked at Mem0, Letta/MemGPT, and similar memory solutions. They all solve a different problem: storing facts from conversations — "user prefers Python," "user is vegetarian." That's key-value memory with semantic search. Useful, but not what we needed.

What we needed was something that learns user patterns implicitly from behavior over time. When a customer corrects a threshold from 85% to 80% three sessions in a row, the agent should just know that next time. When a team always re-runs with stricter filters, the system should pick up on that pattern. So we built an internal API around a simple idea: user corrections are the highest-signal data. Instead of ingesting chat messages and hoping an LLM extracts something, we capture structured events — what the agent produced, what the user changed, what they accepted. A background job periodically runs an LLM pass to extract patterns and builds a confidence-weighted preference profile per user/team/org.

Before each session, the agent fetches the profile and gets smarter over time. The gap as I see it:

Mem0 = memory storage + retrieval. Doesn't learn patterns.

Letta = self-editing agent memory. Closer, but no implicit learning from behavior.

Missing = a preference learning layer that watches how users interact with agents and builds an evolving model. Like a rec engine for agent personalization.

I built this for our domain but the approach is domain-agnostic. Curious if others are hitting the same wall with their agents. Happy to share the architecture, prompts, and confidence scoring approach in detail.

Inspired by this Ask HN: https://news.ycombinator.com/item?id=46834977

But I'm going further back in time to see if there is anybody here who still uses slide rules?

Dear founders,

I'm trying to understand common startup wisdom and it doesn't make sense to me.

For example, if you give me the source code of Dropbox, I don't think I'll cross the 100K ARR in the first year. Maybe, even 1K goal would be a stretch.

What would you do to get an abstract Dropbox to 1M ARR? Start with cold emails? Define ICP and spam them through Linkedin Sales Navigator, aka B2B sales playbook?

I find common advice on validation super generic, which means it doesn't work as it lacks specifics and nuance. For example, competition is good because it proves demand, but "scrappy MVP" doesn't sell in competitive markets.

I'm arriving at the point that the only way to succeed is to get your product embedded into some kind of value delivery pipeline through offline sales: meetups, trade shows, etc. Go to a place where your customers hang out offline.

Did you validate your idea before shipping the product? Did you know in advance that it'll succeed? Did you have to do cold emails to find first customers? Did you feel you had to push your product at first?

We’ve been working on a GPU-first inference platform focused on predictable latency and cost control for production AI workloads.

Some of the engineering problems we ran into:

- GPU cold starts and queue scheduling - Multi-tenant isolation without wasting VRAM - Model loading vs container loading tradeoffs - Batch vs real-time inference routing - Handling burst workloads without long-term GPU reservation - Cost predictability vs autoscaling behavior

We wrote up the architecture decisions, what failed, and what worked.

Happy to answer technical questions - especially around GPU scheduling, inference optimization, and workload isolation.

"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it"

This has been a timely PSA.

We are trying to find a good test management tool for a team that relies a lot on automation and CI. Curious what people here are actually using in real projects. What worked well and what did not? Main things we care about: Easy to use for both dev and QA Works well with automated tests and CI CD Good API and automation support Useful reporting and traceability Handles large test suites without becoming slow Reasonable cost for what it offers Would really like to hear real experiences, including tools you stopped using and why.

Hey HN,

I'm on a quest for a distraction-free writing device and considering a super cheap laptop which I can just run vim/nano on.

I'd like: - Excellent battery life - Good keyboard - Sleep/wake capabilities (why is this so hard with Linux?)

I'm thinking some kind of chromebook? Maybe an old thinkpad?

Hi HN,

Everyone talks about “provably fair” games, raffles, and loot boxes — but in practice, most implementations still require players to trust the server.

Think about it: the server commits a seed, the player sees a hash, and after the game, the result is revealed. Technically, the house could precompute outcomes and cherry-pick them — even if it’s unlikely.

How can you make randomness truly verifiable without relying on a slow blockchain?

Is there a way to combine player input, server input, and public entropy to make it fully auditable?

And crucially, can this be done without killing real-time performance in a game?

I’m curious what people in production are doing — clever commit-reveal models, public beacon sources, hybrid systems? I’d love to see practical approaches that actually remove trust entirely, not just add a “proof” that still requires faith.

Let’s talk solutions.

Wanted to gather some stories about people who were fired because of AI. Not a generic "reorg", what they say in the press release, but honestly, because of AI. Proves?

I've been working on an SMTP/email server lately, and while Google and some others imply a lot of frowny-faces and put quotation marks around tests "passing" for not using a corporate relay, does at least let me communicate with the broader ecosystem.

Phone calls, however, seem like a tougher nut to crack. SIP URIs would let me kinda-sorta communicate with the broader ecosystem, but many phones and software seem to have dropped support for it; only a tiny % of those typically using the PSTN (that is, a "normal phone") would be able to call or receive calls to/from my addresses -- but it would be able to be directly and neatly integrated into the email server, which is a big plus.

I will probably still implement SIP URIs and VOIP support into the email server on principle, but I wonder if anyone has any alternatives to consider. Ideally, I would be able to communicate on the PSTN, but this seems like a lost cause.

Also curious about anyone using VOIP for work whether or not they allow or block SIP URIs from external networks. I maintained our VOIP server at work more than a decade ago, but it was a "side-project" due to working at an SMB where I wore many hats and couldn't specialize in anything; I wasn't even aware of SIP URIs at the time.

Running OpenClaw with Anthrophic API and it burned through ~USD 50 in one day.

What are other OpenClaw users seeing? Anyone found effective ways to cut costs (model tiering, caching, etc.)?

Facebook's feed is nonfunctional. Only some people get notification, even though they have notification for all messages turned on. Only some get the newest posts in their main feed. Sometimes I do get notifications, but only long after an urgent message was posted.

Has anybody successfully moved their local community off of Facebook groups?

I'm thinking about neighbor conversations/events, daycare, kindergarten, kids' classmates, sporting communities, etc.

If so, where did you go? Did you build something yourself or do you self-host some open source project? Did you find a good paid alternative?

I’m trying to pressure-test a pattern I keep seeing in industrial and asset-heavy operations, and I’d value perspectives from people who’ve lived this.

In many environments (manufacturing, equipment rental, oilfield services, aerospace, medical devices), quality and ops work often starts outside formal systems: - inspection notes written by hand - photos on phones - voice notes from the field - emails and spreadsheets coordinating fixes

ERP/QMS systems exist, but under time pressure the work happens elsewhere first. When an audit, customer escalation, or safety question hits, teams scramble to reconstruct what actually happened from scattered artifacts.

A few questions I’m genuinely curious about: - Have you seen environments where this doesn’t happen? What made them different? - Where does reconstruction pain show up the most — audits, customer disputes, asset recertification, something else? - What information tends to get lost when work is summarized or normalized too early? - Who usually carries the burden of “proving” things are fine when something escalates?

I’m not selling anything or looking to promote a tool. I am just trying to understand where reality breaks abstraction in practice.

Would appreciate any firsthand experiences or counterexamples.

Currently the GitHub status page says there is a "Major Outage" for GitHub Actions.

https://www.githubstatus.com/

This is as of 19:58 UTC / 11:58 PST on 2-Feb-2026

Pretty much it. I believe SEO and GEO are the best channels to get the best leads, but is a real pain in the ass in terms of effort for creating articles, indexing ecc.

Any cool hack to actually improve on this?

Hi everyone,

I’m building Persona, a platform to delegate email scheduling to AI. Lately, I’ve been working hard to get those first users on board, but it’s been quite challenging.

I’ve already tried the typical strategies that everybody talks about: cold email, LinkedIn InMail, careful targeting, decent copy. It’s mostly been a dead end. Low open rates, almost no replies.

At this point, I’m not looking for the usual advice you see in blog posts or on reddit. I’m specifically curious about unconventional or non-obvious things that actually worked for you early on, especially things that felt a bit scrappy, weird, or counterintuitive at the time.

If you’ve been through this phase, what genuinely worked and got you your first users?

I vibecoded a couple of iOS apps & a full SaaS (SEOZilla.ai) over the past six months and the honest answer is: my best coding agent sessions from 3-4 months ago would make great submissions. Excellent debugging, catching poor architecture choices, back-and-forth problem solving.

But lately? I mostly write product specs, make simple architecture decisions, and do QA. The agents just... handle it. Across the board, Opus, Sonnet, Cursor, whatever you're using, the jump in the last 2 months has been wild.

Which raises a genuine question: what is YC actually selecting for with this prompt? The most impressive sessions are probably from people using worse tools or tackling harder problems. The founders who've figured out the best workflows might have the most boring transcripts.

Anyone else finding that their "best" agent sessions are now the least interesting ones?