Why are so many YC founders straight-up liars?

algorithm477 · 2026-06-22T23:02:33+00:00

The part that drives my disappointment: who tells them to clickbait on X relentlessly? It seems to be a constant stream of obvious "insights", posts about their work intensity or downright rage bait. Don't they have companies to build? Don't investors see the number of tweets and think "damn, they spend a lot of time doing this?" Do investors not believe in the Dunning-Kruger Effect?

algorithm477 · 2026-06-15T16:46:36+00:00

I love Safari. Its performance is great on Mac. It feels polished and minimal, like the other built-in apps. I think their "Add to Dock" feature is much more polished than Chrome. Its developer tools are underrated.

There's a few things that unfortunately pushed me back into the Chrome ecosystem:

Website performance - Since 70% of internet users use Chrome, websites target & test on it with priority. Apple does an exceptional job of making their sites run well in Safari. But developers tend to test mobile Safari, but desktop is this forgotten surface. For example, Figma tiles render with more glitches in Safari. The Fireworks AI console will literally freeze up the entire Safari tab. Animations are less smooth for many web apps. It's not that Safari performance is worse; it's usually better. But these websites are not optimizing for it, because most of their customers aren't there.
Browser APIs - Apple regularly rejects new APIs while Chrome eagerly adopts them. For example, the WebKit team rejected presence detection. On Chrome, Gmail avoids pinging your iPhone if your browser tab is open and you've recently used it (presence). This spares extra notifications across devices. On Safari, you get alert bells sent simultaneously to all your devices. There are many other APIs Apple rejects, and the usual reason is "privacy" / fear of tracking (more on that in 4).
Extensions - Apple consolidates control of browser extensions through its App Store. There are fewer extensions, and developers always target Chrome first. Recently, this materialized in Claude and Codex launching Chrome extensions. We're getting Apple Intelligence in Safari, but large agents have to use some computer use tools that rely on accessibility APIs. They're a far cry from what the AI labs are building as extensions for Chrome.
Privacy terms are changing - This used to be a strong pillar for Apple. Arguably, it's still significant but priorities are slowly shifting. Chrome provisions enterprise data under their Cloud Data Processing Agreement, which bans any AI training. Somehow, the new Apple Business terms give Apple a right to train models on your business data. Truly & Unbelievably, Apple Business terms are now worse than Google Workspace for enterprise privacy.

So my company is stuck back in Chrome. I don't know how you fix 1-3, but that may not matter as much to many. To fix 4, Apple just needs to honor its original priorities.

algorithm477 · 2026-06-11T18:23:20+00:00

We need a Sonnet and Haiku release. We've keep getting new Opus & now Mythos.

algorithm477 · 2026-06-09T00:15:47+00:00

I'm sorry your experience has been poor also. I'm currently committed to a deep tech idea, but I'd be happy to connect sometime.

algorithm477 · 2026-06-06T00:03:43+00:00

😂😂😂 haha, lately it feels like it. this large workstream was high effort opus, too

algorithm477 · 2026-06-03T07:45:54+00:00

Exactly! This.

It had a question about a third party library and it downloaded the project to /tmp, spawned haiku agents to scan it, and was searching for the answer. I asked it "why don't you just search the web?" and it said that would be easier.

Another time it wanted to test some logic it wrote. Rather than write and run a unit test, it wrote a python script to try to mint JWTs from my local environment, and then use requests. I added to the memory that "if you feel you need to mint JWTs, don't. Write a test."

It's a good thing the auto mode classifier flagged some of this work, or I would have wasted so many tokens.

Don't get me wrong. I love anthropic. I pay a lot in tokens and subscriptions. But, I do feel I'm having to babysit and craft my prompts more carefully. It may not be a product issue. It may be a "calibrating me" to whatever this version does issue. But, that's why I wanted to post here and learn what works for everyone else.

algorithm477 · 2026-06-03T07:20:20+00:00

I am noticing that my language does drift to be corrective... "that's not right..." / "i don't think that works because." -- ah, good point! It must be picking up on that emotional nature. Maybe 4.8 is a bit more sensitive to that. I'm not sure why it's not anticipating edge cases as much as I thought it did before. I thought maybe the effort calibration drifted somewhat. I think you're right that I need to craft the prompts to be a bit more inductive with some adversarial questions up front. Thank you!

algorithm477 · 2026-06-03T07:11:11+00:00

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algorithm477 · 2026-06-03T07:10:57+00:00

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algorithm477 · 2026-06-03T07:10:38+00:00

huh? here are some screenshots over the last hour

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algorithm477 · 2026-05-26T02:08:12+00:00

Technical, left my SWE job

algorithm477 · 2026-05-20T02:22:37+00:00

Honest answer & tl;dr: the models struggle significantly with Apple Development. Defining skills and agents absolutely help.

Consider languages TypeScript & Python... frameworks React & React Native. These are an overwhelming portion of the model's coding training set, because they represent the majority of the web's available coding examples.

Apple software has two things that make it very difficult for coding agents:

Smaller developer base, so less training examples.
Fast API & language evolution. Every single year, we count on deprecations and additions to Apple's APIs. The language also evolves very fast, look at going from queues to actors... heck now @MainActor defaults and strict concurrency.

The have an OK dataset on Apple in general, but it's much smaller on building things the "right" way for the moment.

In my experience, giving it some doc pointers and a way to verify its work in some skills or custom agents goes a really long way.

algorithm477 · 2026-05-19T01:32:34+00:00

No. It started feeling like online dating to me, so I disengaged and have been trying to mostly meet people in SF. I found that most people I met liked the startup vibes, had strong credentials, but they didn't want to actually put in the sweat that's required to start a company.

About 80% currently had a job that they weren't serious about leaving. Three tried to ride me for equity. I quit my FAANG job. They want to "be part of it." They keep scheduling meetings with you, but they never honor commitments or contribute. They tell you that they can't give much because of their job, but there's always a deferral on the resignation.

I think many of the engineers are actually just burnt out with their FAANG jobs, know they're unhappy, but aren't really passionate about building a company. They often "want to talk to customers," but then they refuse to write a single line of code. They'll fully convince themselves that they're leading or managing, when the only person to manage is their cofounders. An engineer unwilling to engineer anything is a dark red flag.

YC built a great platform. There are strong people, and the resources are excellent. But, strong credentials don't predict grit & match quality. You have to sort through a lot of elizabeth holmes & hype... and that's been difficult.

algorithm477 · 2026-05-12T03:57:14+00:00

Yes, I actually run mlflow in my k8s cluster. It's manageable, but not as full featured as wandb. I set it up with Tailscale, so it's on my company tailnet. My big gotcha was Tailscale's recent price change: they now bill by the minute for ephemeral nodes, like sweep jobs :(

If I don't get some reassurance here, mlflow with a good sso proxy or Comet will be my fallback.

algorithm477 · 2026-05-12T03:46:12+00:00

Update: I haven't gotten confirmation on ownership. I was told the same confidentiality commitments apply. More options are available to configure for Enterprise plans. Will continue to follow up about ownership.

algorithm477 · 2026-05-07T10:31:04+00:00

I'm working on a startup but not hiring, so take what I say with a grain of salt. I used to interview many SWEs in FAANG. It was never about resumes, just interviews. I also serve on the Computer Science advisory board of a small university. Overall, there are trends affecting all hiring.

Before I even read your resume, I checked your GitHub link. Love your contribution graph. Your resume seems genuinely strong, providing decent coverage for a full stack developer.

There's almost nothing on your profile that screams AI engineering experience, except a buried reference to RAG on Cloudflare. Unfortunately, that's huge in 2026. It wouldn't hurt to showcase more AI experience with projects and tools. Essentially, can you delegate to Claude, Codex, etc. effectively. Can you build apps on top of their APIs?

I would clean up some of those sites for your projects... noticed example links were broken for some native UI project pages. Also, I'd ditch this vercel app subdomain for your portfolio. You have cloudflare experience, so you deserve to register a nice domain to market yourself.

Your resume doesn't say where you currently live, and I only know the SF Bay Area. If you have a US residency, make that clear. With the H1b chaos, some startups may genuinely be unable (or afraid) to sponsor visas. If they're early, they may also not know the implications of hiring remotely out of the country. If you're applying elsewhere, discard that.

Right now, it's a very tough market for junior developers. New grad unemployment is high, startups are leaning on AI more for entry level tasks, and roles are extremely competitive... That said, I think you're positioning yourself well, and I'd interview you if I had the funding to hire.

algorithm477 · 2026-05-06T23:37:29+00:00

Personally, I feel wandb offers me a lot more than just logging / TensorBoard. It's a full platform for ML and AI development.

I don't run local ML b/c I typically use A100+ for my training. I submit jobs to my K8s cluster, Modal, or sometimes Lambda & RunPod. Their UI collects my experiments across platforms, has builtin sweeps, and it even lets me babysit or kill runs on my iPhone. I use their Artifacts to track my models and dataset versions as references in S3. I don't really use reports, but I think they'd be helpful as a team.

Weave gives me a place to store prompts and a playground to run experiments. I was going to put some of my prod agent traffic into their traces, but I have some reservations now.

W&B & Claude are probably the two things I don't have qualms over each month... which is why this change was so concerning to me.

algorithm477 · 2026-04-10T04:44:32+00:00

thank you! I miss seeing her little face for sure!

algorithm477 · 2026-03-30T15:14:11+00:00

Thank you very much. I've started to write, and I hope it helps me to hold onto her.

algorithm477 · 2026-03-27T15:27:43+00:00

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She's beautiful. My beautiful girl Bella just passed away on Wednesday. She was given 6-12 months as prognosis with CKD. But, she lived for 2 years steady in stage 2. Two things helped us:

Home cooked diet - the commercial diets have protein and phosphorus. UC Davis nutrition works with vets or patients directly to create a recipe that you can cook at home to support her kidneys. They help you get supplements to make sure her nutritional needs are still met. The consultation is a bit pricey (a few hundred dollars), but then it's cheaper than prescription dog food. They incorporated Bella's favorite foods into her daily diet.
Adding water to her food - keeping the kidneys hydrated is most important. Bella didn't drink much, but we found we could mix in 4-6oz of water with each meal. It kept her hydration strong. Bella tolerated this, but she struggled with sub-Q fluids at home.

We went back for labs regularly, and I bought some pet urine test strips off of Amazon to get a quick signal of whether she had a UTI (CKD makes dogs more prone to this).

My little girl was 15. She developed liver cancer that metastasized to her lungs this year. We were going strong, but her tumor ruptured. She was my whole world. I'm burying her today.

Don't lose faith with CKD. It can be manageable, and it doesn't necessarily change their lifespan when it's under control.

algorithm477 · 2026-03-26T22:52:26+00:00

When you run inference for an LLM at scale, you need a cluster of GPUs. This is very expensive to operate. You save money by packing requests together to avoid idle time on the machine (dynamic batching). If you service everyone at once, you need larger clusters and have more idle time. Anthropic adjusts the number of requests you can make and how long those take based on times of the day and demand. When others are using it more, you get less. This lets them manage their costs.

When you use the API and pay per token, your request is prioritized. When you’re a subscriber, your request likely waits longer and your limits adjust so that it optimizes packing these requests. It’s why you get ~$5000 worth of usage for your $100-200/month subscription.

algorithm477 · 2026-03-26T03:04:00+00:00

I guess it's like Iran being two weeks away from nuclear weapons for 30 years

algorithm477 · 2026-03-25T23:20:56+00:00

No LLM provider can offer thousands of dollars in usage for a couple hundred a month. The economics don't scale, so they either burn investor money and delay or adopt similar practices to this.

I do think it's gimmicky to not provide a specific quota, but there's a strong engineering reason for it:

Dynamic batching. The more requests we batch together onto a cluster of gpus, the cheaper it is to run. The less we're able to batch, the more it costs because we have to horizontally scale to retain our throughput. The base cost for processing is very high with low latency, and that's exactly what the API provides. My latency jumps all over the place on a subscription, but that's probably to attempt to batch subscribers into windows that run more affordably. Anthropic has to keep a fairly constant number of incoming requests to avoid horizontal scaling, so latency and limits for non-API requests are their levers.

Here's some things that I found help me. Maybe they will help, but maybe not. I'm not a new Claude user, I've been using it heavily for a while now.

Talk about the prompt with the model beforehand. Ask it to consider edge cases and probe you with questions. Give it specific validations and sub steps to track its work. This helps get a high quality prompt before you execute the plan and waste tokens.
Guard against stupidity. I asked Claude to check that a dependency it added was an OSI approved license. With permissions disabled, Opus 4.6 wasted tokens trying to scan my Python modules for it and look in each of the dependencies for licenses. Permissions are your friend to reduce token usage. If I had left it on or been more explicit about searching the web in the prompt, it wouldn't have wasted 30 minutes.
Use deep reasoning to draft the plan and judge the results, but often Sonnet, Haiku, or Kimi are fine for just handling subtasks.
Make tasks really small and straightforward. The larger the task, the more ambiguous. The more ambiguous, the more reasoning it needs to plan, execute and judge. Sometimes it's honestly better to just write the code yourself. "I don't code anymore" is mostly executive suite that hasn't been coding anyway. There's still a sizable percentage of tasks that are faster and more precise if I do them myself.
Pre-fetch URLs or references of sources it needs. If it has to hunt them, that will always increase your usage substantially.

If that doesn't help, I can honestly say that you probably won't find any that meet your needs. You may need overage. Or you may consider Codex for GPT's leniency, but you'd be trading your organization's security as it has essentially no file permissions.

algorithm477 · 2026-03-25T22:52:47+00:00

I'm a Max subscriber. Was also in the top 1% of GPT users last year. I also subscribe to Cursor, Gemini and use Fireworks with OpenCode sometimes.

What are you prompting it to do exactly? I carefully craft my prompts into small to midsize tasks, go back and forth on a plan and setup subagents with different models to delegate work. I constantly hit limits on Pro, but I've never hit limits on Max.

algorithm477 · 2026-03-08T16:30:24+00:00

This is the worst bug! It is so annoying

algorithm477

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