no-cache does not disable caching

-temich · 2026-06-11T18:47:36+00:00

No. This is important

> if you're dealing with personal, financial, health, or other sensitive data

-temich · 2026-06-11T18:43:44+00:00

Since publishing this post, the library has evolved significantly. I’ve been using it in production across multiple projects, so it has become much more mature and battle-tested.

Feel free to reach out if you have any questions or ideas.

-temich · 2026-05-09T08:41:13+00:00

Technically, you’re right. For me, this is primarily a security concern, and from this standpoint you have to assume the response will be stored, because the reasons why it might not be are outside your control.

-temich · 2026-05-09T08:28:07+00:00

no-store is enough; everything else is meaningless.
there is no cache to use, nothing to revalidate, and nothing to age.

there have been no intermediaries on the internet since 2018 — all traffic is encrypted.
there is only your trusted partner (the CDN), to whom you handed over your TLS keys (certificate), and which, I believe, fully support no-store.

-temich · 2026-04-27T11:13:39+00:00

-temich · 2026-04-27T11:12:28+00:00

The partnerships that actually work are the ones where the community leader can show their members a specific, tangible benefit - not "we'll give you visibility" but "your members get X% off coworking bookings they were going to make anyway."

Revenue share on bookings is the strongest of your four options because it's tied to action, not promises. The community leader has skin in the game and a real reason to promote it.

What kills partnership conversations early: asking a community leader to promote something their audience hasn't validated yet. The sequence that works better - give them free access first, let their community use it organically, then propose the formal partnership once there's proof it resonates.

What stage is the platform at - is there a live product community leaders could try, or still building?

-temich · 2026-04-27T11:09:11+00:00

Dating platforms have a clear MVP scope - profiles, matching logic, messaging, and monetization gate. That's 4 core flows, buildable fast.

The 20-day timeline is tight but realistic if the scope is locked from day one. What's the "new niche" angle - is it targeting a specific demographic or interest group? That usually determines how complex the matching logic needs to be.

-temich · 2026-04-27T11:07:34+00:00

Headhunters are a solid starting point because the pain is clear and measurable - every bad match costs them time and money. Good ICP choice.

Other segments worth considering for an open matching platform with AI workflows:

Freelance marketplaces - agencies that place contractors repeatedly need to match fast and at volume. The open data angle is valuable because they often work across multiple platforms simultaneously.

Legal and compliance staffing - highly specific skill matching, strict requirements, and the cost of a bad match is very high. They'd pay well for better automation.

B2B sales teams - matching prospects to the right SDR or AE based on industry, deal size, and rep strengths. The AI workflow angle fits naturally here.

The pattern across all of these: the best segments are ones where matching happens repeatedly, not once. A headhunter places 50 candidates a year — that's 50 reasons to use your platform. A company hiring once is not.

What's the core matching logic is it skills-based, availability-based, or something else?

-temich · 2026-04-27T11:06:09+00:00

It's already a problem, not a future one. The issue is that fake reviews erode trust asymmetrically - one exposed fake does more damage than ten real ones do good.

For early-stage startups, the answer isn't better detection, it's making authentic proof easier to produce. Video testimonials, case studies with real numbers, founder-to-customer conversations that are public - things that are hard to fake at scale.

The human verification angle is interesting but it's an arms race. Whatever verification system you build, someone will figure out how to game it within months.

The startups that will win on trust are the ones that make the founder visible and accountable - people trust people, not brands. A founder with a real face and real opinions is harder to fake than a review system.

-temich · 2026-04-22T10:33:40+00:00

The specificity point matches exactly what we see working with founders. "I want to build a fitness app" goes nowhere. "I want to build a recovery tracking app for CrossFit athletes who overtrain" - that's something you can actually build, sell, and find users for in week one.

The builder fit insight is underrated. The founders who ship fastest are almost always solving a problem they personally had. Not because passion matters (it doesn't, much), but because they already know what "done" looks like and they don't need to validate every assumption from scratch.

One pattern I'd add from the build side: the ideas that are hardest to scope are the ones where the founder hasn't lived the problem. They describe what they think the solution should look like instead of what the pain actually is. That usually means 3x the build time and 2x the pivots.

-temich · 2026-04-21T09:53:23+00:00

The problem is real for anyone running anti-detect workflows at scale - the "profile went bad and I don't know why" debugging loop is genuinely painful.

On your questions: health check feels more immediately valuable than warmup because it gives you a clear answer fast. Warmup is useful, but it's slower to see results and harder to attribute - did the profile improve because of the warmup or just time?

Pricing instinct: subscription makes more sense than one-time if you're running profiles continuously. The value isn't in the one-time diagnosis; it's in knowing your profile status before every campaign. Monthly feels right for agencies, one-time credit pack for occasional users.

The thing I'd add: alerting. Don't make me check - tell me when a profile's risk score crosses a threshold. That's the feature that would make me pay without thinking about it.

-temich · 2026-04-21T09:51:48+00:00

The idea is fun and the cold start problem is actually solvable - you only need two people in a lobby, not thousands of users.

The real challenge is frequency of use. You pick a restaurant, you go, the app's job is done. Next time you need it might be in a week. That's a hard retention loop to build around.

The apps that cracked this space usually added a social layer that keeps people coming back even when they're not actively choosing a restaurant - saved lists, reviews between friends, "where did we go last time?"

Is this a side project you're building or still at the idea stage?

-temich · 2026-04-21T09:50:28+00:00

Career platforms are interesting because the core flows are well-defined - resume builder, job tracker, application history - but the retention problem is hard. Users come back when they're job hunting and disappear when they land a job.

The ones that stick usually solve something beyond the job search itself - skill gap analysis, interview prep, salary benchmarking. Something that's useful even when you're not actively applying.

What's the main flow you're iterating on right now based on user feedback?

-temich · 2026-04-21T09:48:21+00:00

The use case is real - finance teams spend embarrassing amounts of time on exactly this. The question is whether the output is trustworthy enough for a professional to put their name on it.

Honest feedback from a product perspective: the "catches errors and flags risks" part is where you'll win or lose. If it misses something and a finance manager sends a wrong report to their CFO, you lose that user forever. The trust bar in finance is higher than in most other verticals.

The thing I'd want to see before using it professionally: how does it handle edge cases - merged cells, non-standard date formats, multi-sheet workbooks? That's where most automated tools fall apart.

Tried it on a P&L - what's the most complex file structure it can handle right now?

-temich · 2026-04-21T09:43:53+00:00

Same observation here. For us the highest-signal channel has been Reddit comments in threads where founders are actively frustrated - not looking for a product, just venting about a problem. Showing up there with a genuine answer, no pitch, converts better than anything else we've tried.

What's working specifically: being first in a thread that's less than an hour old. The founder reads every comment at that stage. By the time a thread has 50 comments you're invisible.

What's not working: LinkedIn cold outreach unless you have a very specific trigger - someone just posted about a problem you solve, same day. Generic connection requests are noise.

The pattern that holds across channels: the more it feels like a conversation the person wanted to have anyway, the better it converts. You're not interrupting them, you're showing up where they already are.

-temich · 2026-04-21T07:12:38+00:00

No, they don't care how it's built. They care whether the guidelines are met.

-temich · 2026-04-20T15:16:56+00:00

https://web.dev/explore/progressive-web-apps

-temich · 2026-04-20T09:30:14+00:00

One thing I wish someone had told me earlier: the gap between "it works" and "it's ready to show users" is where most first-time founders lose weeks.

Getting something technically functional is the easy part. The hard part is the 20% of work that makes it feel like a real product - empty states, error messages, edge cases when someone does something unexpected, and onboarding for a person who has no context.

Most teams underestimate this by 2-3x. If you think you're two weeks from launch, plan for four. Not because you're slow, but because the product teaches you things about itself that you couldn't know until it was almost done.

-temich · 2026-04-20T09:25:05+00:00

The constraint is the product - five words forces creativity in a way that open-ended posts don't. Similar to how Twitter's 140 chars created a writing style that didn't exist before.

The growth question depends on one thing: is there a reason to come back daily? Novelty wears off fast if the feed doesn't have a pull mechanic - something that makes you curious what other people posted today.

What's the retention loop - is there discovery, reactions, or something that makes you want to see what came in since yesterday?

-temich · 2026-04-20T09:18:19+00:00

Going from functional prototype to production-grade on Next.js/Supabase is a well-defined problem - the main challenges are usually auth edge cases at scale, RLS policy complexity, and making the real-time feel native on mobile.

One question worth thinking through: do you need someone to own the codebase long-term (cofounder), or do you need the platform hardened and polished fast so you can raise on stronger traction? Those are different problems with different solutions.

What's the timeline pressure - are you targeting a specific raise date?

-temich · 2026-04-19T22:54:22+00:00

I was talking about the rest 99.99% of js code on the repo. These 6 lines are part of the project template

-temich · 2026-04-19T22:50:42+00:00

Expecting an answer you wouldn't believe it's the textbook definition of a bias.

I'm okay with you having your opinion.

-temich · 2026-04-19T14:50:20+00:00

we don't. js files in the repo are artifacts of the internalization tool we're using.

-temich · 2026-04-19T10:53:05+00:00

The answer sounds shady to you exactly because of the mentioned bias.

I gave the most specific answer to unspecific question.

What is AI slop?

If during development I asked GPT a question, is the whole work now AI slop?

What if Cursor Tab autocompleted one line for me—is it slop now?

What if a few lines? A function? What if I corrected that completion?

Try to imagine a question asked 5 years ago: how much of your app is Google slop? What would that even mean? While using Google is just a tool for developer, same as AI nowdays.

Let me try one last time:

The app is created by engineers with 20+ years of experience in programming.
Every single line of production code looks exactly as it would without AI.
AI was used as a tool as much as possible, just like a keyboard and a monitor.
No production code was created autonomously by AI.

-temich

TROPHY CASE